Artificial Neural Networks-Based HVDC System for Transient Stability Enhancement of Nigeria Power Grid

Artificial Neural Networks-Based HVDC System for Transient Stability Enhancement of Nigeria Power Grid | 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 Artificial Neural Networks-Based HVDC System for Transient Stability Enhancement of Nigeria Power Grid Chibuike Peter Ohanu, Uche C. Ogbuefi, Emenike Ejiogu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5435081/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The increasing disturbances in power system networks present significant challenges to maintaining stability, especially in grid-tied generators, posing risks to synchronism and grid resilience. In this paper, an artificial neural networks (ANN) based high-voltage direct current system is applied as a FACTS device to improve the transient stability of a multi-generator power system. A comprehensive analysis was conducted on Nigeria 330kV 40-bus transmission network using the MATLAB-based Power System Analysis Toolbox (PSAT). An initial system assessment used the Newton-Raphson power flow method and eigenvalue analysis to establish base case stability metrics and to reveal critical stability issues. This analysis shows a significant voltage reduction of 0.70 per unit (pu) and synchronism loss under fault conditions on the test system. The application of proportional-integral (PI) controller-based HVDC systems improved the system to a minimum voltage magnitude of 0.80 pu, which is below the statutory transmission voltage limit of 0.95 to 1.05 pu. Therefore, an ANN-based HVDC system was along the lines and this shows significant improvement with a three-phase fault clearing time reduced to 2 seconds, compared to the 3 seconds obtained with the PI controller-based device. This method improved voltage profile to a minimum voltage magnitude of 0.98 pu, improving system stability and synchronism. The results highlight a 27.8% improvement in voltage magnitude, affirming the proposed method as a superior alternative for transient stability enhancement. This paper provides valuable insights into the integration of intelligent systems for sustainable power grid operation and improved fault resilience in complex power networks. Voltage stability Transient stability HVDC system Artificial neural network Transmission network Improvement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5435081","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379627989,"identity":"587c9a46-51c5-415d-9b8a-e439b6772fee","order_by":0,"name":"Chibuike Peter Ohanu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACCcY2BgaDBCCLB8S1gYjykKAljRgtDGxAEq7lMGEt/LOb2x7zFKTJMbD3Hvxc2HY+cbt0A+ODt20MefIOOCy5c7DdmMcgx5iB51yy9My224k75xxgNpzbxlBseAC7FgOJxDZpHoOKxAaJHANpXqCWDTcS2IAMhsSNDfi11AO1GP/mbTsH0sL+mwgtOQkMEjlmQMMPgG1hBmmZj8P7EjcS2w3nGKQZtvGcMbMG+sd4w43EZsk55yQSN+AKsRnpzx68+ZMsz8/eY3ybp8xOdsON5IMf3pTZJM7H4TA4YEMwGUFqJRgMDhDQggnkCdkyCkbBKBgFIwUAAFwrVs91aHGtAAAAAElFTkSuQmCC","orcid":"","institution":"University of Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Chibuike","middleName":"Peter","lastName":"Ohanu","suffix":""},{"id":379627990,"identity":"8d8a18b8-5047-45fd-ada1-ffd0c12c9dd9","order_by":1,"name":"Uche C. 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In this paper, an artificial neural networks (ANN) based high-voltage direct current system is applied as a FACTS device to improve the transient stability of a multi-generator power system. A comprehensive analysis was conducted on Nigeria 330kV 40-bus transmission network using the MATLAB-based Power System Analysis Toolbox (PSAT). An initial system assessment used the Newton-Raphson power flow method and eigenvalue analysis to establish base case stability metrics and to reveal critical stability issues. This analysis shows a significant voltage reduction of 0.70 per unit (pu) and synchronism loss under fault conditions on the test system. The application of proportional-integral (PI) controller-based HVDC systems improved the system to a minimum voltage magnitude of 0.80 pu, which is below the statutory transmission voltage limit of 0.95 to 1.05 pu. Therefore, an ANN-based HVDC system was along the lines and this shows significant improvement with a three-phase fault clearing time reduced to 2 seconds, compared to the 3 seconds obtained with the PI controller-based device. This method improved voltage profile to a minimum voltage magnitude of 0.98 pu, improving system stability and synchronism. The results highlight a 27.8% improvement in voltage magnitude, affirming the proposed method as a superior alternative for transient stability enhancement. 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