Statistics and law analysis of personal safety accidents of power grid enterprises in China from 2014 to 2023

Statistics and law analysis of personal safety accidents of power grid enterprises in China from 2014 to 2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Statistics and law analysis of personal safety accidents of power grid enterprises in China from 2014 to 2023 Yifeng Jiang, Yan LI, Zhen Li, Longming Sun, Aitao Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6095804/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract To objectively analyze the patterns and characteristics of personal safety incidents in China’s power grid enterprises, the study examines the patterns of personal safety incidents in power grid companies from 2014 to 2023, focusing on accident types, spatial distribution, temporal distribution, professional fields, and causative factors.The results indicate that from 2014 to 2023, the overall trends in the number of personal safety incidents and fatalities in power grid enterprises remained stable. However, there is a slight rebound in the occurrence of major incidents. The primary types of accidents involve electric shocks, falls from heights, and injuries caused by falling objects.Guangxi, Yunnan, and Inner Mongolia are identified as provinces with a high incidence of accidents. The South China region has a notably higher rate of personal safety incidents compared to other regions. April and May are peak months for accidents each year, with high-risk periods occurring daily from 9:00 to 10:00 and 15:00 to 16:00. The frequency of accidents in the field of power production is significantly higher than in power construction, with incident counts 30.82% higher and fatalities 5.32% higher.Unsafe human behaviors and management deficiencies are the primary causes of accidents. Among unsafe behaviors, incorrect safety operating procedures account for the highest number of incidents. Among management deficiencies, the most common cause of accidents is the failure to implement or communicate safety measures effectively. Earth and environmental sciences/Environmental social sciences/Energy and society/Energy security Physical sciences/Mathematics and computing/Statistics power grid enterprise personal safety grade of accidents statistical regularity type of accidents Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Electricity is a foundational industry that is closely tied to the national economy and people's livelihoods, while power grid companies are one of the country's essential infrastructures. Personal safety accidents in power grid companies have a significant impact on the safe production of these companies [ 1 – 3 ] . Due to the unique nature of the power industry, personal safety accidents in power grid companies tend to follow certain patterns. Therefore, conducting statistical analysis on past personal safety accidents in power grid companies and summarizing the patterns of these accidents is of great importance for improving the safety production level of power grid companies and ensuring the life and property safety of workers. Currently, many scholars both domestically and internationally have conducted statistical studies on personal injury and fatal accidents in power companies. Cawley et al. [ 4 ] conducted a statistical analysis of power line electrocution accidents in the United States from 1992 to 2002 and found that 99.1% of fatal cases were caused by electrocution, with contact with overhead power lines being the primary cause. He Danxin [ 5 ] performed a statistical analysis on accidents occurring over a ten-year period in a certain power company and concluded that human errors were the main cause of safety accidents in power production. Fan Yunxiao et al. [ 6 ] classified both the direct and indirect causes of 333 production safety accidents in power supply companies between 1961 and 2008, finding that the accidents primarily reflected a lack of systematic safety management within the company. Fan Xianxin [ 7 ] conducted a statistical analysis on accidents occurring between 1996 and 2010 in a certain power grid construction company, considering factors such as accident types, causes, and job positions. Based on the main problems identified in the company’s safety production management, he proposed preventive management strategies and recommendations to avoid personal safety accidents. Liang Zhixiang [ 8 ] explored the causes and preventive measures for personal safety accidents in power companies. Yan Yuqiong et al. [ 9 ] used statistical methods to study the personal accidents in power companies nationwide from 2016 to 2021, analyzing accident time and space distribution, accident types, companies involved, and operational processes to identify the characteristics and patterns of such incidents. These studies reveal the basic patterns of personal accidents in power grid companies, analyzing factors such as unsafe behaviors and management deficiencies. However, research on personal accidents in China’s power grid companies lacks depth in areas such as time span and analytical dimensions, and systematic analysis is insufficient. In response, this study conducts a statistical analysis of personal safety accidents in China’s power grid companies from 2014 to 2023, based on six dimensions: overall accident situation, accident types, time distribution, geographic distribution, professional fields, and causes. By deeply investigating the patterns of accident occurrence, this study aims to provide useful references for improving the safety management level and accident prevention and control capabilities of power grid companies. 2. Overview of personal safety accidents in power grid companies in recent years To ensure the authenticity and accuracy of the analysis, the statistical data is sourced from the official website of the National Energy Administration, the "Compilation of Power Safety Accident Events" book, the CNKI (China National Knowledge Infrastructure) database, media official websites, and other publicly available resources [ 10 – 13 ] (the statistical data excludes Hong Kong, Macau, and Taiwan, unless otherwise stated). The statistical data used in this study is relatively complete, with broad coverage, and can serve as a data support for studying the patterns and characteristics of personal accidents in power grid companies. From 2014 to 2023, a total of 118 personal safety accidents occurred in power grid companies nationwide, resulting in 169 deaths. Among them, there were 13 major accidents, accounting for 11.02% of the total number of accidents, with 47 deaths, accounting for 27.81% of the total fatalities. No major or catastrophic accidents occurred during this period. The accident data for the past decade is shown in Figs. 1 and 2 . As shown in Fig. 1 , the average number of accidents per year during 2014–2023 was 11.8, with an average of 16.9 deaths per year. The highest number of accidents occurred in 2016, with 19 accidents and 26 deaths. The fewest accidents occurred in 2022, with 6 incidents and 8 deaths. Overall, the year with the highest number of accidents had 3.17 times the number of incidents as the year with the fewest accidents, and the year with the highest number of deaths had 3.38 times the number of fatalities as the year with the fewest deaths. As shown in Fig. 2 , in the past 10 years, there were significant accidents in 6 of those years. No major accidents occurred in 2014, 2019, 2021, and 2022. The years 2015, 2016, and 2017 experienced the highest number of major accidents, with 3 incidents each. Overall, from 2014 to 2023, the number of personal safety accidents and fatalities in power grid enterprises showed a fluctuating trend, but remained within a relatively stable range. 3. Statistical analysis of personal safety accidents in power grid enterprises 3.1. Statistical Analysis of Accident Types According to the "Classification Standard for Employee Casualty Accidents" (GB6441-1986) [ 14 ] , the types of accidents relevant to power grid enterprises include seven categories: falls from heights, electric shocks, being struck by objects, mechanical injuries, roof collapse, poisoning and suffocation, and explosions. Based on the accident data and characteristics of power grid enterprises, two additional categories—pole toppling and tower tilting—have been included, bringing the total number of accident types to nine, as shown in Table 1 . Table 1 Classification of accident types and accidents Accident Types Number of accidents/incidents Number of deaths/person Number of fatalities in a single accident/person Electric shocks 62 76 1.23 Falls from heights 27 38 1.41 Being struck by objects 12 16 1.33 Pole toppling 7 12 1.71 Tower tilting 3 10 3.33 Poisoning and suffocation 3 9 3.00 Mechanical injuries 1 1 1.00 Roof collapse 2 5 2.50 Explosions 1 2 2 The number of accidents and fatalities for each accident type are shown in Fig. 3 . As seen in the figure, electric shock accidents occurred the most, with 62 incidents, accounting for 52.5% of the total number of accidents, and resulting in 76 deaths, or 45% of the total fatalities. Falls from heights ranked second, with 27 incidents, accounting for 22.9% of the total accidents, and causing 38 deaths, or 22.5% of the total fatalities. Struck by objects ranked third, with 12 incidents, representing 10.2% of the total accidents, and resulting in 16 deaths, or 9.5% of the total fatalities. Electric shock, falls from heights, and struck by objects together account for 85.6% of all accidents and 77% of all fatalities, making these three types of accidents the primary focus for safety prevention in power grid enterprises. The number of deaths per accident for different types of incidents is shown in Fig. 4. As seen in Fig. 4, the highest number of fatalities per incident occurs in tower collapse accidents, with an average of 3.33 deaths per accident. This is followed by poisoning and asphyxiation accidents, with 3 deaths per accident, and roof collapse accidents, with 2.5 deaths per accident. Although the occurrences of tower collapse, poisoning/asphyxiation, and roof collapse accidents are relatively few, they result in significant damage, high casualties, and severe consequences, which warrant considerable attention. 3.2. Statistical Analysis of Accident Regional Distribution 3.2.1. Statistical Analysis by Province The statistics and analysis of personal accidents in power grid enterprises across different provinces in China from 2014 to 2023 are shown in Figs. 4 and 5 . Among them, Beijing, Shanghai, and Qinghai have not reported any personal accidents in power grid enterprises. As shown in Fig. 5 , the national average number of accidents per province is 4.21. Ten provinces (autonomous regions) had accident numbers above the national average, namely: Guangxi (15 accidents), Inner Mongolia (13 accidents), Yunnan (10 accidents), Shaanxi (9 accidents), Sichuan (8 accidents), Guangdong (7 accidents), Anhui (6 accidents), Fujian (6 accidents), Hebei (5 accidents), and Hainan (5 accidents). Among these, Guangxi, Yunnan, and Inner Mongolia had the highest number of accidents, with each province (autonomous region) reporting 10 or more accidents, accounting for 32.3% of the total accidents nationwide. Conversely, five provinces—Tianjin, Liaoning, Shanxi, Henan, Hubei, and Jiangsu—had relatively fewer accidents, each with only one incident. As shown in Fig. 6 , the national average number of fatalities per province is 6.04. Eleven provinces (autonomous regions) reported fatalities above the national average, namely: Guangxi (16 deaths), Inner Mongolia (15 deaths), Shaanxi (12 deaths), Yunnan (12 deaths), Guangdong (12 deaths), Anhui (11 deaths), Hunan (11 deaths), Sichuan (9 deaths), Hebei (8 deaths), Jiangxi (8 deaths), and Shandong (7 deaths). Among these, seven provinces (autonomous regions)—Guangxi, Anhui, Inner Mongolia, Shaanxi, Yunnan, Guangdong, and Hunan—had relatively higher fatality numbers, with each reporting 10 or more deaths, accounting for 52.66% of the total fatalities nationwide. Conversely, three provinces—Tianjin, Hubei, and Shanxi—had fewer fatalities, each with only one death. 3.2.2. Regional Statistics China is divided into seven geographical regions: East China, South China, North China, Central China, Southwest China, Northwest China, and Northeast China. The statistics for accident numbers and fatalities across these seven regions are presented in Table 2 . Table 2 Accidents by region Region Number of accidents/incidents Average number of accidents/ incidents Number of deaths/ person Average number of deaths per province/person Southwest China 25 5.00 30 6.00 South China 27 9.00 33 11.00 East China 16 2.67 32 5.33 North China 20 4.00 25 5.00 Northwest China 16 3.20 20 4.00 Central China 9 2.25 22 5.50 Northeast China. 5 1.67 7 2.33 As shown in Table 1 , in terms of the number of accidents, the South China region has the highest number of incidents, with 27 accidents, while the Northeast region has the fewest, with 5 accidents. The South China region also has the highest average number of accidents per province, at 9 accidents per province, whereas the Northeast region has the lowest average, with 1.67 accidents per province. In terms of fatalities, the East China region has the highest number of deaths, with 47 fatalities, while the Northeast region has the fewest fatalities, with 7 deaths. The South China region also has the highest average number of fatalities per province, with 11 deaths per province, while the Northeast region has the lowest average, with 2.33 fatalities per province. Overall, the South China region has the highest number of accidents and fatalities, with the highest average number of accidents and fatalities per province. This indicates that personal safety incidents are more frequent in this region, and there is a need to strengthen safety incident prevention and control in power grid enterprises in South China. 3.3. Statistical Analysis of Accident Time Distribution 3.3.1. Statistical Analysis by Quarter According to the statistical analysis of personal accidents in power grid enterprises from 2014 to 2023, based on the quarters in which the accidents occurred, the accident situation for each quarter is shown in Fig. 7 . As seen in the figure, the overall situation of personal accidents in power grid enterprises follows a unimodal distribution.The second quarter is the peak period for accidents, with 51 accidents occurring and 80 fatalities. The third quarter follows, with 29 accidents and 41 fatalities. The fourth quarter ranks third, with 23 accidents and 28 fatalities. The first quarter has the fewest incidents, with 15 accidents and 20 fatalities. 3.3.2. Statistical Analysis by Month According to the statistical analysis of personal accidents in power grid enterprises from 2014 to 2023, based on the months in which the accidents occurred, the accident situation for each month is shown in Fig. 8 . As seen in the figure, the average number of accidents per month from 2014 to 2023 is 9.83, with an average of 14.08 fatalities per month. The number of accidents in April through July and in August is higher than the monthly average, and the number of fatalities from April to July is also above the monthly average.Among these months, April and May have peak accident numbers and fatalities, coinciding with the critical spring inspection period, during which the workload in power grid operations is high, safety risks are elevated, and personal accidents occur frequently. To effectively prevent accidents, power grid enterprises should strengthen safety supervision during the spring inspection period. Conversely, the number of accidents and fatalities is at its lowest in January and February due to the winter shutdown and the reduced workload during the Chinese New Year holiday, which lowers safety risks and leads to fewer personal accidents. 3.3.3. Statistical Analysis by Time Period The statistics for 118 personal safety accidents in power grid enterprises from 2014 to 2023, categorized by hourly intervals, are shown in Fig. 9 . As seen in Fig. 9 , personal safety accidents in power grid enterprises predominantly occur during the daytime, with an overall bimodal distribution. Accidents are relatively fewer between 21:00–01:00 and 02:00–05:00, while accidents occur more frequently between 09:00–10:00 in the morning and 15:00–16:00 in the afternoon.This pattern can be attributed to the high mental concentration and intense physical labor required in power grid operations. After working for a period in the morning and afternoon, workers tend to experience short fatigue periods, during which their energy, attention, and reaction times decrease. This significantly increases the risk of accidents occurring. 3.4. Statistical analysis of specialized areas of accidents According to the classification standards for power-related personal injury and fatality accidents published by the National Energy Administration, the personal safety accidents in power grid enterprises are categorized into two professional fields: power construction and power production. The statistical analysis of personal safety accidents in power grid enterprises from 2014 to 2023, based on professional fields, is presented in Table 3 , showing the number of accidents, the proportion of fatalities in each field, and the number of fatalities per accident for each field. Table 3 Accidents in different areas of specialization Specialized areas Percentage of accidents Percentage of deaths Number of fatal-ities in a single accident/person Power production 60.17% 52.66% 1.25 Power construction 39.83% 47.34% 1.70 As shown in the table, the number of accidents in power production is the highest, accounting for 60.17% of the total accidents, while the number of accidents in power construction accounts for 39.83% of the total. The number of fatalities in power production is also the highest, representing 52.66% of the total fatalities, while fatalities in power construction account for 47.34% of the total. The average number of fatalities per accident is highest in power construction, with 1.70 fatalities per incident, followed by power production, with 1.25 fatalities per incident. 3.5. Statistical Analysis of Accident Causes 3.5.1. Main Causes of Accidents According to the 4M theory, accident causes are categorized into four main factors: unsafe human behavior, unsafe material conditions, management loopholes, and environmental defects [ 15 ] . The basic situation of accidents caused by these different factors is shown in Table 4 . Table 4 Causes of accidents by type of accidents Accident Causes Accident Types Unsafe human behavior Unsafe material conditions Management loopholes Environmental defects Electric shocks 62 11 27 0 Falls from heights 24 7 14 4 Being struck by objects 8 6 7 0 Pole toppling 5 4 4 1 Tower tilting 2 1 3 0 Poisoning and suffocation 1 0 1 0 Mechanical injuries 3 0 3 2 Roof collapse 1 2 1 0 Explosions 0 1 1 0 Total 106 32 61 7 Percentage 51.46% 15.53% 29.61% 3.40% As shown in Table 4 , the majority of accidents occur due to unsafe human behavior, with 106 incidents, accounting for 51.46% of the total accidents. The second most common cause is management loopholes, leading to 61 accidents, or 29.61% of the total. Unsafe material conditions caused 32 accidents, accounting for 15.53% of the total accidents. Environmental defects were the cause of 7 accidents, or 3.40% of the total. Furthermore, as shown in Fig. 10 , electric shock accidents caused by unsafe human behavior resulted in the highest number of fatalities, with 76 deaths. Falls from heights followed, with 35 fatalities. Pole toppling, tower tilting, and poisoning/suffocation ranked third, with 9 fatalities each. Electric shock accidents caused by unsafe material conditions resulted in 16 fatalities, with falls from heights following at 11 deaths, and being struck by objects ranking third with 10 fatalities. Poisoning/suffocation accidents caused by environmental defects resulted in 7 fatalities, with falls from heights following at 4 deaths, and pole toppling ranking third with 3 fatalities. Finally, management loopholes led to the highest number of electric shock fatalities, with 31 deaths, followed by falls from heights with 23 fatalities, and tower tilting ranking third with 10 fatalities. 3.5.2. Direct Causes of Accidents The four main accident causes—unsafe human behavior, unsafe material conditions, management loopholes, and environmental defects—can be further subdivided. Based on the 118 existing accident investigation reports, the statistics for the number of accidents caused by the subdivided causes are shown in Table 5 . Table 5 Number of accidents by cause of accident Accident Causes Concrete meaning Number of accidents/incidents Unsafe human behavior Improper safety procedures 29 Failure to correctly implement safety precautions or technical measures 25 Insufficient safe distances from live equipment 19 Failure to conduct risk and hazard assessments before work 11 Unauthorized operations 10 Unauthorized work without permits or work orders 7 Operational errors 7 Unauthorized expansion of the work scope 6 Failure to verify electrical lines 6 Unauthorized changes to construction plans 5 Unsafe material condtions Defects in mechanical equipment quality 18 Failure of safety protection devices on mechanical equipment 12 Falling objects 1 Defects in safety protection equipment 1 Management loopholes Failure to implement or communicate safety measures 28 Blind organization of operations 18 Lack of on-site supervision 16 Environmental defects Weather conditions such as strong winds 6 The surrounding work environment 1 Unsafe human behavior includes improper safety procedures, failure to correctly implement safety precautions or technical measures, insufficient safe distances from live equipment, unauthorized expansion of the work scope, unauthorized operations, failure to conduct risk and hazard assessments before work, unauthorized work without permits or work orders, operational errors, unauthorized changes to construction plans, and failure to verify electrical lines. According to the table, the most common cause of accidents is improper safety procedures, with 29 incidents, followed by failure to correctly implement safety precautions or technical measures, leading to 25 incidents. Insufficient safe distance from live equipment ranks third, with 19 incidents. Unsafe material conditions include defects in mechanical equipment quality, failure of safety protection devices on mechanical equipment, falling objects, and defects in safety protection equipment. According to the table, the most common cause of accidents is mechanical equipment quality defects, with 18 incidents, followed by the failure of safety protection devices on mechanical equipment, which caused 12 incidents. Management loopholes include failure to implement or communicate safety measures, blind organization of operations, and lack of on-site supervision. The table shows that the most common cause of accidents is the failure to implement or communicate safety measures, with 28 incidents, followed by blind organization of operations, which caused 18 incidents, and lack of on-site supervision, which led to 16 incidents. Environmental defects include weather conditions, such as strong winds, and the surrounding work environment. According to the table, weather factors like strong winds caused 6 accidents, which is a relatively high proportion, making it a significant environmental factor contributing to accidents. 4. Conclusion 1) From 2014 to 2023, the number of personal safety accidents in power grid enterprises fluctuated, but remained relatively stable overall. Major accidents accounted for about one-tenth of the total incidents, but the fatalities from these accidents represented about one-fifth of the total fatalities. These incidents remain a key focus for accident prevention and control in the present and future. 2) Fall accidents, electric shock accidents, and being struck by objects are the primary types of accidents requiring focused prevention efforts. While tower tilting, poisoning and suffocation, and roof collapse accidents occur less frequently, they result in significant damage, high casualties, and severe consequences, warranting heightened vigilance. 3) Guangxi, Yunnan, and Inner Mongolia have a higher number of accidents. The South China region exhibits both high accident numbers and fatalities. These provinces and regions should prioritize strengthening personal safety management and control. 4) The number of accidents in April through July and August exceeds the monthly average, with fatalities from April to July being above the monthly average. In terms of power grid operation times, accident prevention efforts should focus on the periods from 09:00–10:00 and 15:00–16:00. 5) The vast majority of accidents occur due to unsafe human behavior and management loopholes. Among unsafe human behaviors, the primary cause is improper safety procedures. In terms of unsafe material conditions, the main cause is mechanical equipment quality defects. Environmental defects, especially weather factors like strong winds, are the primary cause in that category. In terms of management deficiencies, the main issue is the failure to implement or communicate safety measures. Declarations Corresponding Author Zhen Li, Email Address: [email protected] Author Contribution Y.F. J.and Y.L. conceived the structure of the paper and acquired data,Z.L.utilized software to analyze data and write first drafts of paper, L.M.S and A.T. Z.reconciled data and made substantive changes.All authors reviewed the manuscript. Acknowledgments This work was supported in part by China Electric Power Research Institute Limited Think Tank Research Project on Grid Operation Safety Risk Analysis and Control Research (C342JZ240001); State Grid Corporation Limited Headquarters Technical Research Service Project on Research and Practice of Safety Production Supervision Work (SGZB0000AJJS2400335); and China Electric Power Research Institute Limited Yard Digital New Infrastructure Project (YA42X Z230001). Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Ma, Y. X. Analyzing the problems and countermeasures in safety production management of electric power enterprises. J. pub Relat. world 2022 , (2 1):105–106 (in Chinese). Editorial Committee. Construction and Practice of Intrinsic Safety Capacity of Power Grid Enterprises,pp 12–15 (China Electr. Pow., 2018). (in Chinese). Chen, L. P. Statistics and analysis of safety accidents in electric power production. J. Pow equip. manag , 20 21 ,(04):106–108 (in Chinese). Cawley, J. C. G. T. Homce.Trends in electrical injury in the US 1992–2002. J. IEEE Trans. Ind. Appl. 2008 , 44 (4): 962–972. 10.1109/TIA.2008 . 926229. He, D. X. Research on the influence factors and management countermeasures of human error in the production of electric power enterprises. J. commun. World , 2014 (3): 44–45 (in Chinese). Fan, Y. X. et al. Research on accident classification of power supply enterprises based on accident causation theory, An Ch'uan Yu Huan Ching Hsueh Pao J. Saf. Environ. 10 (4), 141–144 (2010). (in Chinese). X.-X. Fan. Statistical analysis of accidents in power grid infrastructure construction enterprises and preventive measures, Chung-kuo An Ch'uan Sheng Ch'an K'o Hsueh Chi Shu. J. Saf. Sci. Technol. , 8 (09):159–163 (2012). (in Chinese). Liang, Z. X. Analysis of the causes of personal safety accidents in electric power enterprises and discussion of its preventive measures, J. Sci. technol. Innov. Appli.,2016,(34):169(in Chinese). 10.19981/j.cn23- 1581/g3.2016.34.144 Yan, Y. Q. et al. Statistical and regular analysis of personal accidents in China's electric power enterprises from 2016 to 2021, J. Saf. , 44 (4): 46–51 (2023). (in Chinese). DOI: 10.19737/j.cnki.issn 1002-3631.2023.04.008 . National Energy Administration, Electric Power Safety Supervision Department. Compilation of national electric power accidents and electric power safety incidents,pp. Beijing: China Telegraph (Beijing) Audiovisual Publishing House, 2024(in Chinese). National Energy Administration, Department of Electric Power Safety Supervision. National Electricity Accident and Electricity Safety Incident Database,pp. Beijing:China Telegraph (Beijing) Audiovisual Publishing House, 2023(in Chinese). National Electricity Safety Production Situation in September. National Energy Administration official website [EB/OL]. (2021/10/30)[2024/11/25].htt (2021). ps://www.nea.gov.cn/2021-10/30/c_1310343193.htm(in Chinese). Accident Analysis Report for the Second Quarter of 2021. Official website of National Energy Administration [EB/OL]. /09/09) [2024/11/25]. https://www.nea.gov.cn/2021-09/09/c_1310177594.htm (2021). (in Chinese). GB 6441-86. Standard for Classification of Casualties and Injuries of Enterprise Employees,S. National Bureau of Standards, 1986-05-31(in Chinese). Ma, H., Zeng, Y. H. & Ma, G. F. Analysis of operation safety of electrical equipment based on 4M elements,J. Electri. Indust.,2020,(06):60–62 (in Chinese). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 May, 2025 Reviews received at journal 20 May, 2025 Reviews received at journal 11 May, 2025 Reviewers agreed at journal 11 May, 2025 Reviewers agreed at journal 11 May, 2025 Reviewers invited by journal 17 Apr, 2025 Editor assigned by journal 17 Apr, 2025 Editor invited by journal 09 Mar, 2025 Submission checks completed at journal 06 Mar, 2025 First submitted to journal 24 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6095804","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424998740,"identity":"46fe0d2e-d0f2-4d90-b2e9-bd8e16b47f1b","order_by":0,"name":"Yifeng Jiang","email":"","orcid":"","institution":"State Grid Corporation of China (China)","correspondingAuthor":false,"prefix":"","firstName":"Yifeng","middleName":"","lastName":"Jiang","suffix":""},{"id":424998747,"identity":"ef4324ab-c030-4d17-933c-cf918fd74959","order_by":1,"name":"Yan LI","email":"","orcid":"","institution":"State Grid Corporation of China (China)","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"LI","suffix":""},{"id":424998752,"identity":"dbe77e27-4177-430b-a22e-51e374f364be","order_by":2,"name":"Zhen Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIie3QsQrCMBCA4SuBdDmM40mhbyBEAk5FX8VSqKujmy1CXHwAwfdwjhZ08QEUHMziXB9A0VmQxs0h33w/xx2A5/2hFoja3J8JE2FZ2tol4QA9u+J52FlWc0WOiVLId0KexrqNTokwOSGaCM5WA8Eg7haNW8yeiC4qWKf6OoFM9U1TEpSapLxlLEoXksCkm8aEMU6jUTXTna0mdEo4Z9KYiiEFrgliYMsiZ4Tp+8nS4RYhjlA9ioQNDwdr6+kgbkw+yN/GPc/zvC9engw+VJT6nxgAAAAASUVORK5CYII=","orcid":"","institution":"China University of Mining and Technology (Beijing)","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Li","suffix":""},{"id":424998754,"identity":"1c9950a8-4031-4ee3-b604-71723b25d8b3","order_by":3,"name":"Longming Sun","email":"","orcid":"","institution":"Comprehensive Service Center of State Grid Jiangsu Electric Power Co. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Longming","middleName":"","lastName":"Sun","suffix":""},{"id":424998755,"identity":"421fe5ea-1067-4f06-b9ab-91e6c714a0bf","order_by":4,"name":"Aitao Zhou","email":"","orcid":"","institution":"China University of Mining and Technology (Beijing)","correspondingAuthor":false,"prefix":"","firstName":"Aitao","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-02-24 10:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6095804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6095804/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30567-4","type":"published","date":"2026-01-05T15:59:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78159413,"identity":"49e88272-36d0-43c2-a82b-417f86bfa44c","added_by":"auto","created_at":"2025-03-10 13:08:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367205,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of accidents and fatalities by year\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/6bb8764bec4fe4786df39630.png"},{"id":78159411,"identity":"235f7661-fae8-4955-8343-7141c3a67b4a","added_by":"auto","created_at":"2025-03-10 13:08:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230131,"visible":true,"origin":"","legend":"\u003cp\u003eAccident level statistics\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/01a12daf3f30f59cd991e057.png"},{"id":78159399,"identity":"b66e2866-a971-4f52-b856-aec98324662a","added_by":"auto","created_at":"2025-03-10 13:08:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2519977,"visible":true,"origin":"","legend":"\u003cp\u003eStatistics on the occurrence of accidents of different accident types\u003c/p\u003e","description":"","filename":"floatimage311.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/b900e4fcf65142d0e66eadb9.png"},{"id":78159405,"identity":"70317943-d58e-49c6-afec-c86545ef4a8d","added_by":"auto","created_at":"2025-03-10 13:08:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309501,"visible":true,"origin":"","legend":"\u003cp\u003eStatistics on the number of fatalities in a single accident for different types of accidents\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/eda4817be4be7305804d2125.png"},{"id":78159421,"identity":"0246051d-c66f-4fcd-abe1-661393f0f572","added_by":"auto","created_at":"2025-03-10 13:08:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256032,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of accidents by province\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/e935507540420e1cfc2d1664.png"},{"id":78159416,"identity":"e9794b27-271d-4e3b-98f6-e8232612c8b0","added_by":"auto","created_at":"2025-03-10 13:08:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":262345,"visible":true,"origin":"","legend":"\u003cp\u003eAccident fatalities by province\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/476f1da7ecb80ab0d7d5ae23.png"},{"id":78159418,"identity":"ec8207f0-f141-4322-8e62-0e027ccef7e8","added_by":"auto","created_at":"2025-03-10 13:08:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":213567,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Accidents and Fatalities by Quarter\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/dbaf1b04cb5feb0bc38538f8.png"},{"id":78159437,"identity":"b93c0b0f-f44a-4012-be39-5bf80421637c","added_by":"auto","created_at":"2025-03-10 13:08:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":207586,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of accidents and fatalities by month\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/ac1aa5bb81805248d1850720.png"},{"id":78159323,"identity":"9cb81608-02c5-4c0b-9957-8cbe0818f347","added_by":"auto","created_at":"2025-03-10 13:08:26","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":316391,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of accidents and fatalities by time period\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/894273bcb42c21b35c2758fe.png"},{"id":78159403,"identity":"853cf7fa-765d-4e77-84f3-af3359774b8c","added_by":"auto","created_at":"2025-03-10 13:08:31","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":401357,"visible":true,"origin":"","legend":"\u003cp\u003eFatalities in different types of accidents caused by different contributing factors\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/ea5619a310f5101ab791fe7d.jpeg"},{"id":100070180,"identity":"6d26f765-8807-4425-80d1-1fd354cad98f","added_by":"auto","created_at":"2026-01-12 16:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6159296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6095804/v1/6e2d83e6-6e2b-4e10-b222-54db62a7bc72.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Statistics and law analysis of personal safety accidents of power grid enterprises in China from 2014 to 2023","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eElectricity is a foundational industry that is closely tied to the national economy and people's livelihoods, while power grid companies are one of the country's essential infrastructures. Personal safety accidents in power grid companies have a significant impact on the safe production of these companies\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Due to the unique nature of the power industry, personal safety accidents in power grid companies tend to follow certain patterns. Therefore, conducting statistical analysis on past personal safety accidents in power grid companies and summarizing the patterns of these accidents is of great importance for improving the safety production level of power grid companies and ensuring the life and property safety of workers.\u003c/p\u003e \u003cp\u003eCurrently, many scholars both domestically and internationally have conducted statistical studies on personal injury and fatal accidents in power companies. Cawley et al. \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e conducted a statistical analysis of power line electrocution accidents in the United States from 1992 to 2002 and found that 99.1% of fatal cases were caused by electrocution, with contact with overhead power lines being the primary cause. He Danxin \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e performed a statistical analysis on accidents occurring over a ten-year period in a certain power company and concluded that human errors were the main cause of safety accidents in power production. Fan Yunxiao et al. \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e classified both the direct and indirect causes of 333 production safety accidents in power supply companies between 1961 and 2008, finding that the accidents primarily reflected a lack of systematic safety management within the company. Fan Xianxin \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e conducted a statistical analysis on accidents occurring between 1996 and 2010 in a certain power grid construction company, considering factors such as accident types, causes, and job positions. Based on the main problems identified in the company\u0026rsquo;s safety production management, he proposed preventive management strategies and recommendations to avoid personal safety accidents. Liang Zhixiang \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e explored the causes and preventive measures for personal safety accidents in power companies. Yan Yuqiong et al. \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e used statistical methods to study the personal accidents in power companies nationwide from 2016 to 2021, analyzing accident time and space distribution, accident types, companies involved, and operational processes to identify the characteristics and patterns of such incidents.\u003c/p\u003e \u003cp\u003eThese studies reveal the basic patterns of personal accidents in power grid companies, analyzing factors such as unsafe behaviors and management deficiencies. However, research on personal accidents in China\u0026rsquo;s power grid companies lacks depth in areas such as time span and analytical dimensions, and systematic analysis is insufficient. In response, this study conducts a statistical analysis of personal safety accidents in China\u0026rsquo;s power grid companies from 2014 to 2023, based on six dimensions: overall accident situation, accident types, time distribution, geographic distribution, professional fields, and causes. By deeply investigating the patterns of accident occurrence, this study aims to provide useful references for improving the safety management level and accident prevention and control capabilities of power grid companies.\u003c/p\u003e"},{"header":"2. Overview of personal safety accidents in power grid companies in recent years","content":"\u003cp\u003eTo ensure the authenticity and accuracy of the analysis, the statistical data is sourced from the official website of the National Energy Administration, the \"Compilation of Power Safety Accident Events\" book, the CNKI (China National Knowledge Infrastructure) database, media official websites, and other publicly available resources \u003csup\u003e[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e (the statistical data excludes Hong Kong, Macau, and Taiwan, unless otherwise stated). The statistical data used in this study is relatively complete, with broad coverage, and can serve as a data support for studying the patterns and characteristics of personal accidents in power grid companies.\u003c/p\u003e \u003cp\u003eFrom 2014 to 2023, a total of 118 personal safety accidents occurred in power grid companies nationwide, resulting in 169 deaths. Among them, there were 13 major accidents, accounting for 11.02% of the total number of accidents, with 47 deaths, accounting for 27.81% of the total fatalities. No major or catastrophic accidents occurred during this period. The accident data for the past decade is shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the average number of accidents per year during 2014\u0026ndash;2023 was 11.8, with an average of 16.9 deaths per year. The highest number of accidents occurred in 2016, with 19 accidents and 26 deaths. The fewest accidents occurred in 2022, with 6 incidents and 8 deaths. Overall, the year with the highest number of accidents had 3.17 times the number of incidents as the year with the fewest accidents, and the year with the highest number of deaths had 3.38 times the number of fatalities as the year with the fewest deaths.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in the past 10 years, there were significant accidents in 6 of those years. No major accidents occurred in 2014, 2019, 2021, and 2022. The years 2015, 2016, and 2017 experienced the highest number of major accidents, with 3 incidents each.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, from 2014 to 2023, the number of personal safety accidents and fatalities in power grid enterprises showed a fluctuating trend, but remained within a relatively stable range.\u003c/p\u003e"},{"header":"3. Statistical analysis of personal safety accidents in power grid enterprises","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Statistical Analysis of Accident Types\u003c/h2\u003e\n \u003cp\u003eAccording to the \u0026quot;Classification Standard for Employee Casualty Accidents\u0026quot; (GB6441-1986) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, the types of accidents relevant to power grid enterprises include seven categories: falls from heights, electric shocks, being struck by objects, mechanical injuries, roof collapse, poisoning and suffocation, and explosions. Based on the accident data and characteristics of power grid enterprises, two additional categories\u0026mdash;pole toppling and tower tilting\u0026mdash;have been included, bringing the total number of accident types to nine, as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClassification of accident types and accidents\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccident Types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of accidents/incidents\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of deaths/person\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of fatalities in a single accident/person\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectric shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFalls from heights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeing struck by objects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePole toppling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTower tilting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoisoning and suffocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanical injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoof collapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExplosions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe number of accidents and fatalities for each accident type are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. As seen in the figure, electric shock accidents occurred the most, with 62 incidents, accounting for 52.5% of the total number of accidents, and resulting in 76 deaths, or 45% of the total fatalities. Falls from heights ranked second, with 27 incidents, accounting for 22.9% of the total accidents, and causing 38 deaths, or 22.5% of the total fatalities. Struck by objects ranked third, with 12 incidents, representing 10.2% of the total accidents, and resulting in 16 deaths, or 9.5% of the total fatalities. Electric shock, falls from heights, and struck by objects together account for 85.6% of all accidents and 77% of all fatalities, making these three types of accidents the primary focus for safety prevention in power grid enterprises.\u003c/p\u003e\n \u003cp\u003eThe number of deaths per accident for different types of incidents is shown in Fig.\u0026nbsp;4. As seen in Fig.\u0026nbsp;4, the highest number of fatalities per incident occurs in tower collapse accidents, with an average of 3.33 deaths per accident. This is followed by poisoning and asphyxiation accidents, with 3 deaths per accident, and roof collapse accidents, with 2.5 deaths per accident. Although the occurrences of tower collapse, poisoning/asphyxiation, and roof collapse accidents are relatively few, they result in significant damage, high casualties, and severe consequences, which warrant considerable attention.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Statistical Analysis of Accident Regional Distribution\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1. Statistical Analysis by Province\u003c/h2\u003e\n \u003cp\u003eThe statistics and analysis of personal accidents in power grid enterprises across different provinces in China from 2014 to 2023 are shown in Figs. 4 and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Among them, Beijing, Shanghai, and Qinghai have not reported any personal accidents in power grid enterprises.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the national average number of accidents per province is 4.21. Ten provinces (autonomous regions) had accident numbers above the national average, namely: Guangxi (15 accidents), Inner Mongolia (13 accidents), Yunnan (10 accidents), Shaanxi (9 accidents), Sichuan (8 accidents), Guangdong (7 accidents), Anhui (6 accidents), Fujian (6 accidents), Hebei (5 accidents), and Hainan (5 accidents). Among these, Guangxi, Yunnan, and Inner Mongolia had the highest number of accidents, with each province (autonomous region) reporting 10 or more accidents, accounting for 32.3% of the total accidents nationwide. Conversely, five provinces\u0026mdash;Tianjin, Liaoning, Shanxi, Henan, Hubei, and Jiangsu\u0026mdash;had relatively fewer accidents, each with only one incident.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the national average number of fatalities per province is 6.04. Eleven provinces (autonomous regions) reported fatalities above the national average, namely: Guangxi (16 deaths), Inner Mongolia (15 deaths), Shaanxi (12 deaths), Yunnan (12 deaths), Guangdong (12 deaths), Anhui (11 deaths), Hunan (11 deaths), Sichuan (9 deaths), Hebei (8 deaths), Jiangxi (8 deaths), and Shandong (7 deaths). Among these, seven provinces (autonomous regions)\u0026mdash;Guangxi, Anhui, Inner Mongolia, Shaanxi, Yunnan, Guangdong, and Hunan\u0026mdash;had relatively higher fatality numbers, with each reporting 10 or more deaths, accounting for 52.66% of the total fatalities nationwide. Conversely, three provinces\u0026mdash;Tianjin, Hubei, and Shanxi\u0026mdash;had fewer fatalities, each with only one death.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. Regional Statistics\u003c/h2\u003e\n \u003cp\u003eChina is divided into seven geographical regions: East China, South China, North China, Central China, Southwest China, Northwest China, and Northeast China. The statistics for accident numbers and fatalities across these seven regions are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAccidents by region\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of accidents/incidents\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage number of accidents/ incidents\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of deaths/ person\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage number of deaths per province/person\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthwest China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthwest China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNortheast China.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, in terms of the number of accidents, the South China region has the highest number of incidents, with 27 accidents, while the Northeast region has the fewest, with 5 accidents. The South China region also has the highest average number of accidents per province, at 9 accidents per province, whereas the Northeast region has the lowest average, with 1.67 accidents per province. In terms of fatalities, the East China region has the highest number of deaths, with 47 fatalities, while the Northeast region has the fewest fatalities, with 7 deaths. The South China region also has the highest average number of fatalities per province, with 11 deaths per province, while the Northeast region has the lowest average, with 2.33 fatalities per province.\u003c/p\u003e\n \u003cp\u003eOverall, the South China region has the highest number of accidents and fatalities, with the highest average number of accidents and fatalities per province. This indicates that personal safety incidents are more frequent in this region, and there is a need to strengthen safety incident prevention and control in power grid enterprises in South China.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Statistical Analysis of Accident Time Distribution\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1. Statistical Analysis by Quarter\u003c/h2\u003e\n \u003cp\u003eAccording to the statistical analysis of personal accidents in power grid enterprises from 2014 to 2023, based on the quarters in which the accidents occurred, the accident situation for each quarter is shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. As seen in the figure, the overall situation of personal accidents in power grid enterprises follows a unimodal distribution.The second quarter is the peak period for accidents, with 51 accidents occurring and 80 fatalities. The third quarter follows, with 29 accidents and 41 fatalities. The fourth quarter ranks third, with 23 accidents and 28 fatalities. The first quarter has the fewest incidents, with 15 accidents and 20 fatalities.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2. Statistical Analysis by Month\u003c/h2\u003e\n \u003cp\u003eAccording to the statistical analysis of personal accidents in power grid enterprises from 2014 to 2023, based on the months in which the accidents occurred, the accident situation for each month is shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. As seen in the figure, the average number of accidents per month from 2014 to 2023 is 9.83, with an average of 14.08 fatalities per month. The number of accidents in April through July and in August is higher than the monthly average, and the number of fatalities from April to July is also above the monthly average.Among these months, April and May have peak accident numbers and fatalities, coinciding with the critical spring inspection period, during which the workload in power grid operations is high, safety risks are elevated, and personal accidents occur frequently. To effectively prevent accidents, power grid enterprises should strengthen safety supervision during the spring inspection period. Conversely, the number of accidents and fatalities is at its lowest in January and February due to the winter shutdown and the reduced workload during the Chinese New Year holiday, which lowers safety risks and leads to fewer personal accidents.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3. Statistical Analysis by Time Period\u003c/h2\u003e\n \u003cp\u003eThe statistics for 118 personal safety accidents in power grid enterprises from 2014 to 2023, categorized by hourly intervals, are shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAs seen in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, personal safety accidents in power grid enterprises predominantly occur during the daytime, with an overall bimodal distribution. Accidents are relatively fewer between 21:00\u0026ndash;01:00 and 02:00\u0026ndash;05:00, while accidents occur more frequently between 09:00\u0026ndash;10:00 in the morning and 15:00\u0026ndash;16:00 in the afternoon.This pattern can be attributed to the high mental concentration and intense physical labor required in power grid operations. After working for a period in the morning and afternoon, workers tend to experience short fatigue periods, during which their energy, attention, and reaction times decrease. This significantly increases the risk of accidents occurring.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Statistical analysis of specialized areas of accidents\u003c/h2\u003e\n \u003cp\u003eAccording to the classification standards for power-related personal injury and fatality accidents published by the National Energy Administration, the personal safety accidents in power grid enterprises are categorized into two professional fields: power construction and power production. The statistical analysis of personal safety accidents in power grid enterprises from 2014 to 2023, based on professional fields, is presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, showing the number of accidents, the proportion of fatalities in each field, and the number of fatalities per accident for each field.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAccidents in different areas of specialization\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecialized areas\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage of accidents\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage of deaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of fatal-ities in a single accident/person\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePower production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePower construction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAs shown in the table, the number of accidents in power production is the highest, accounting for 60.17% of the total accidents, while the number of accidents in power construction accounts for 39.83% of the total. The number of fatalities in power production is also the highest, representing 52.66% of the total fatalities, while fatalities in power construction account for 47.34% of the total. The average number of fatalities per accident is highest in power construction, with 1.70 fatalities per incident, followed by power production, with 1.25 fatalities per incident.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Statistical Analysis of Accident Causes\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.1. Main Causes of Accidents\u003c/h2\u003e\n \u003cp\u003eAccording to the 4M theory, accident causes are categorized into four main factors: unsafe human behavior, unsafe material conditions, management loopholes, and environmental defects\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The basic situation of accidents caused by these different factors is shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\" style=\"margin-right: calc(3%); width: 97%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCauses of accidents by type of accidents\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 32.2475%;\"\u003e\u0026nbsp;Accident Causes\u003cbr\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Accident Types\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003eUnsafe human\u003c/p\u003e\n \u003cp\u003ebehavior\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnsafe material\u003c/p\u003e\n \u003cp\u003econditions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eManagement\u003c/p\u003e\n \u003cp\u003eloopholes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003cp\u003edefects\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eElectric shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eFalls from heights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eBeing struck by objects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003ePole toppling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eTower tilting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003ePoisoning and suffocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eMechanical injuries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eRoof collapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eExplosions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 32.2475%;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 16.6124%;\"\u003e\n \u003cp\u003e51.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the majority of accidents occur due to unsafe human behavior, with 106 incidents, accounting for 51.46% of the total accidents. The second most common cause is management loopholes, leading to 61 accidents, or 29.61% of the total. Unsafe material conditions caused 32 accidents, accounting for 15.53% of the total accidents. Environmental defects were the cause of 7 accidents, or 3.40% of the total.\u003c/p\u003e\n \u003cp\u003eFurthermore, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, electric shock accidents caused by unsafe human behavior resulted in the highest number of fatalities, with 76 deaths. Falls from heights followed, with 35 fatalities. Pole toppling, tower tilting, and poisoning/suffocation ranked third, with 9 fatalities each. Electric shock accidents caused by unsafe material conditions resulted in 16 fatalities, with falls from heights following at 11 deaths, and being struck by objects ranking third with 10 fatalities. Poisoning/suffocation accidents caused by environmental defects resulted in 7 fatalities, with falls from heights following at 4 deaths, and pole toppling ranking third with 3 fatalities. Finally, management loopholes led to the highest number of electric shock fatalities, with 31 deaths, followed by falls from heights with 23 fatalities, and tower tilting ranking third with 10 fatalities.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.2. Direct Causes of Accidents\u003c/h2\u003e\n \u003cp\u003eThe four main accident causes\u0026mdash;unsafe human behavior, unsafe material conditions, management loopholes, and environmental defects\u0026mdash;can be further subdivided. Based on the 118 existing accident investigation reports, the statistics for the number of accidents caused by the subdivided causes are shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of accidents by cause of accident\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccident Causes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConcrete meaning\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of accidents/incidents\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003eUnsafe human behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproper safety procedures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFailure to correctly implement safety precautions or technical measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsufficient safe distances from live equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFailure to conduct risk and hazard assessments before work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnauthorized operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnauthorized work without permits or work orders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOperational errors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnauthorized expansion of the work scope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFailure to verify electrical lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnauthorized changes to construction plans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eUnsafe material condtions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefects in mechanical equipment quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFailure of safety protection devices on mechanical equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFalling objects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefects in safety protection equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eManagement loopholes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFailure to implement or communicate safety measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlind organization of operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of on-site supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEnvironmental defects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeather conditions such as strong winds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe surrounding work environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eUnsafe human behavior includes improper safety procedures, failure to correctly implement safety precautions or technical measures, insufficient safe distances from live equipment, unauthorized expansion of the work scope, unauthorized operations, failure to conduct risk and hazard assessments before work, unauthorized work without permits or work orders, operational errors, unauthorized changes to construction plans, and failure to verify electrical lines. According to the table, the most common cause of accidents is improper safety procedures, with 29 incidents, followed by failure to correctly implement safety precautions or technical measures, leading to 25 incidents. Insufficient safe distance from live equipment ranks third, with 19 incidents.\u003c/p\u003e\n \u003cp\u003eUnsafe material conditions include defects in mechanical equipment quality, failure of safety protection devices on mechanical equipment, falling objects, and defects in safety protection equipment. According to the table, the most common cause of accidents is mechanical equipment quality defects, with 18 incidents, followed by the failure of safety protection devices on mechanical equipment, which caused 12 incidents.\u003c/p\u003e\n \u003cp\u003eManagement loopholes include failure to implement or communicate safety measures, blind organization of operations, and lack of on-site supervision. The table shows that the most common cause of accidents is the failure to implement or communicate safety measures, with 28 incidents, followed by blind organization of operations, which caused 18 incidents, and lack of on-site supervision, which led to 16 incidents.\u003c/p\u003e\n \u003cp\u003eEnvironmental defects include weather conditions, such as strong winds, and the surrounding work environment. According to the table, weather factors like strong winds caused 6 accidents, which is a relatively high proportion, making it a significant environmental factor contributing to accidents.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003e1) From 2014 to 2023, the number of personal safety accidents in power grid enterprises fluctuated, but remained relatively stable overall. Major accidents accounted for about one-tenth of the total incidents, but the fatalities from these accidents represented about one-fifth of the total fatalities. These incidents remain a key focus for accident prevention and control in the present and future.\u003c/p\u003e \u003cp\u003e2) Fall accidents, electric shock accidents, and being struck by objects are the primary types of accidents requiring focused prevention efforts. While tower tilting, poisoning and suffocation, and roof collapse accidents occur less frequently, they result in significant damage, high casualties, and severe consequences, warranting heightened vigilance.\u003c/p\u003e \u003cp\u003e3) Guangxi, Yunnan, and Inner Mongolia have a higher number of accidents. The South China region exhibits both high accident numbers and fatalities. These provinces and regions should prioritize strengthening personal safety management and control.\u003c/p\u003e \u003cp\u003e4) The number of accidents in April through July and August exceeds the monthly average, with fatalities from April to July being above the monthly average. In terms of power grid operation times, accident prevention efforts should focus on the periods from 09:00\u0026ndash;10:00 and 15:00\u0026ndash;16:00.\u003c/p\u003e \u003cp\u003e5) The vast majority of accidents occur due to unsafe human behavior and management loopholes. Among unsafe human behaviors, the primary cause is improper safety procedures. In terms of unsafe material conditions, the main cause is mechanical equipment quality defects. Environmental defects, especially weather factors like strong winds, are the primary cause in that category. In terms of management deficiencies, the main issue is the failure to implement or communicate safety measures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCorresponding Author\u003c/h2\u003e \u003cp\u003eZhen Li, Email Address: [email protected]\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.F. J.and Y.L. conceived the structure of the paper and acquired data,Z.L.utilized software to analyze data and write first drafts of paper, L.M.S and A.T. Z.reconciled data and made substantive changes.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis work was supported in part by China Electric Power Research Institute Limited Think Tank Research Project on Grid Operation Safety Risk Analysis and Control Research (C342JZ240001); State Grid Corporation Limited Headquarters Technical Research Service Project on Research and Practice of Safety Production Supervision Work (SGZB0000AJJS2400335); and China Electric Power Research Institute Limited Yard Digital New Infrastructure Project (YA42X Z230001).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMa, Y. X. Analyzing the problems and countermeasures in safety production management of electric power enterprises. \u003cem\u003eJ. pub Relat. world 2022\u003c/em\u003e, (2 1):105\u0026ndash;106 (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEditorial Committee. \u003cem\u003eConstruction and Practice of Intrinsic Safety Capacity of Power Grid Enterprises,pp\u003c/em\u003e12\u0026ndash;15 (China Electr. Pow., 2018). (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L. P. Statistics and analysis of safety accidents in electric power production. \u003cem\u003eJ. Pow equip. manag\u003c/em\u003e, \u003cb\u003e20 21\u003c/b\u003e,(04):106\u0026ndash;108 (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCawley, J. C. G. T. Homce.Trends in electrical injury in the US 1992\u0026ndash;2002. \u003cem\u003eJ. IEEE Trans. Ind. Appl. 2008\u003c/em\u003e, \u003cb\u003e44\u003c/b\u003e(4): 962\u0026ndash;972. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TIA.2008\u003c/span\u003e\u003cspan address=\"10.1109/TIA.2008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 926229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, D. X. Research on the influence factors and management countermeasures of human error in the production of electric power enterprises. \u003cem\u003eJ. commun. World\u003c/em\u003e, \u003cb\u003e2014\u003c/b\u003e(3): 44\u0026ndash;45 (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, Y. X. et al. Research on accident classification of power supply enterprises based on accident causation theory, An Ch'uan Yu Huan Ching Hsueh Pao J. \u003cem\u003eSaf. Environ.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (4), 141\u0026ndash;144 (2010). (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX.-X. Fan. Statistical analysis of accidents in power grid infrastructure construction enterprises and preventive measures, Chung-kuo An Ch'uan Sheng Ch'an K'o Hsueh Chi Shu. \u003cem\u003eJ. Saf. Sci. Technol.\u003c/em\u003e, \u003cb\u003e8\u003c/b\u003e (09):159\u0026ndash;163 (2012). (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang, Z. X. Analysis of the causes of personal safety accidents in electric power enterprises and discussion of its preventive measures, J. Sci. technol. Innov. Appli.,2016,(34):169(in Chinese). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.19981/j.cn23- 1581/g3.2016.34.144\u003c/span\u003e\u003cspan address=\"10.19981/j.cn23- 1581/g3.2016.34.144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, Y. Q. et al. Statistical and regular analysis of personal accidents in China's electric power enterprises from 2016 to 2021,\u003cem\u003eJ. Saf.\u003c/em\u003e, \u003cb\u003e44\u003c/b\u003e(4): 46\u0026ndash;51 (2023). (in Chinese). DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.19737/j.cnki.issn 1002-3631.2023.04.008\u003c/span\u003e\u003cspan address=\"10.19737/j.cnki.issn 1002-3631.2023.04.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Energy Administration, Electric Power Safety Supervision Department. Compilation of national electric power accidents and electric power safety incidents,pp. Beijing: China Telegraph (Beijing) Audiovisual Publishing House, 2024(in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Energy Administration, Department of Electric Power Safety Supervision. National Electricity Accident and Electricity Safety Incident Database,pp. Beijing:China Telegraph (Beijing) Audiovisual Publishing House, 2023(in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Electricity Safety Production Situation in September. National Energy Administration official website [EB/OL]. (2021/10/30)[2024/11/25].htt (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eps://www.nea.gov.cn/2021-10/30/c_1310343193.htm(in\u003c/span\u003e\u003cspan address=\"http://ps://www.nea.gov.cn/2021-10/30/c_1310343193.htm(in\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAccident Analysis Report for the Second Quarter of 2021. Official website of National Energy Administration [EB/OL]. /09/09) [2024/11/25]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nea.gov.cn/2021-09/09/c_1310177594.htm\u003c/span\u003e\u003cspan address=\"https://www.nea.gov.cn/2021-09/09/c_1310177594.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021). (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGB 6441-86. Standard for Classification of Casualties and Injuries of Enterprise Employees,S. National Bureau of Standards, 1986-05-31(in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, H., Zeng, Y. H. \u0026amp; Ma, G. F. Analysis of operation safety of electrical equipment based on 4M elements,J. Electri. Indust.,2020,(06):60\u0026ndash;62 (in Chinese).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"power grid enterprise, personal safety, grade of accidents, statistical regularity, type of accidents","lastPublishedDoi":"10.21203/rs.3.rs-6095804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6095804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo objectively analyze the patterns and characteristics of personal safety incidents in China\u0026rsquo;s power grid enterprises, the study examines the patterns of personal safety incidents in power grid companies from 2014 to 2023, focusing on accident types, spatial distribution, temporal distribution, professional fields, and causative factors.The results indicate that from 2014 to 2023, the overall trends in the number of personal safety incidents and fatalities in power grid enterprises remained stable. However, there is a slight rebound in the occurrence of major incidents. The primary types of accidents involve electric shocks, falls from heights, and injuries caused by falling objects.Guangxi, Yunnan, and Inner Mongolia are identified as provinces with a high incidence of accidents. The South China region has a notably higher rate of personal safety incidents compared to other regions. April and May are peak months for accidents each year, with high-risk periods occurring daily from 9:00 to 10:00 and 15:00 to 16:00. The frequency of accidents in the field of power production is significantly higher than in power construction, with incident counts 30.82% higher and fatalities 5.32% higher.Unsafe human behaviors and management deficiencies are the primary causes of accidents. Among unsafe behaviors, incorrect safety operating procedures account for the highest number of incidents. Among management deficiencies, the most common cause of accidents is the failure to implement or communicate safety measures effectively.\u003c/p\u003e","manuscriptTitle":"Statistics and law analysis of personal safety accidents of power grid enterprises in China from 2014 to 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-10 13:08:10","doi":"10.21203/rs.3.rs-6095804/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-22T07:54:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T17:50:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-12T01:21:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13613347731595889222465576020993341875","date":"2025-05-11T22:21:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155287240283411661202805864895873295510","date":"2025-05-11T16:40:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-17T09:54:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-17T09:50:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-10T03:19:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-06T09:57:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-24T10:15:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd7ebd86-6124-4efe-a8ef-1bfc906124ac","owner":[],"postedDate":"March 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45295118,"name":"Earth and environmental sciences/Environmental social sciences/Energy and society/Energy security"},{"id":45295119,"name":"Physical sciences/Mathematics and computing/Statistics"}],"tags":[],"updatedAt":"2026-01-12T16:10:36+00:00","versionOfRecord":{"articleIdentity":"rs-6095804","link":"https://doi.org/10.1038/s41598-025-30567-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-05 15:59:14","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-03-10 13:08:10","video":"","vorDoi":"10.1038/s41598-025-30567-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30567-4","workflowStages":[]},"version":"v1","identity":"rs-6095804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6095804","identity":"rs-6095804","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}