Ai-based Fault Detection and Predictive Maintenance for Power Transmission Networks in Remote Areas Using Catboost

Ai-based Fault Detection and Predictive Maintenance for Power Transmission Networks in Remote Areas Using Catboost | 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 Research Article Ai-based Fault Detection and Predictive Maintenance for Power Transmission Networks in Remote Areas Using Catboost Dac-Nhuong Le, Bakri Hossain Awaji, Osamah AlDhafer, Khalid Alkhattabi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9206809/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Power transmission networks play a serious role in maintaining the stability and reliability of electrical systems. Fault detection and predictive maintenance are essential to ensure continuous operation and minimize downtimes, but traditional fault detection methods face challenges, particularly in remote areas where manual inspections are impractical. This paper presents a framework to enhance the steadiness and efficacy of power transmission networks through advanced fault detection and predictive maintenance. The proposed framework begins with data collection from power transmission sensors, including voltage, current, and temperature readings, along with historical fault records. Next, data pre-processing is performed using median imputation to handle missing values and categorical encoding to transform non-numeric data into numerical form. Feature extraction follows, where time-domain features like Peak-to-Peak Value, RMS, and Zero-Crossing Rate are computed to detect potential faults. The CatBoost model is then trained on the extracted features, and hyperparameter optimization is conducted using the Coati Optimization Algorithm. Once trained, the model performs fault detection and prediction, identifying faults such as Transformer Failures, Overheating, and Line Breakages. The model is assessed using metrics like accuracy of 99.42%, precision of 99.37%, recall of 99.40%, and F1-score of 99.38%. The framework achieves high performance in detecting faults and can be deployed in power transmission systems for proactive maintenance, reducing reliance on manual inspections, improving system reliability, and addressing challenges in remote locations. Fault Detection Predictive Maintenance Power Transmission Networks CatBoost Machine Learning Transformer Failures Overheating Line Breakages Coati Optimization Algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 May, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 28 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 24 Mar, 2026 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-9206809","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620244776,"identity":"5150865a-0148-4d6f-8259-2e9862633c12","order_by":0,"name":"Dac-Nhuong 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Fault detection and predictive maintenance are essential to ensure continuous operation and minimize downtimes, but traditional fault detection methods face challenges, particularly in remote areas where manual inspections are impractical. This paper presents a framework to enhance the steadiness and efficacy of power transmission networks through advanced fault detection and predictive maintenance. The proposed framework begins with data collection from power transmission sensors, including voltage, current, and temperature readings, along with historical fault records. Next, data pre-processing is performed using median imputation to handle missing values and categorical encoding to transform non-numeric data into numerical form. Feature extraction follows, where time-domain features like Peak-to-Peak Value, RMS, and Zero-Crossing Rate are computed to detect potential faults. 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