Power Quality Solutions for Rail Transport using AI-based Unified Power Quality Conditioners

Power Quality Solutions for Rail Transport using AI-based Unified Power Quality Conditioners | 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 Power Quality Solutions for Rail Transport using AI-based Unified Power Quality Conditioners D. K. Nishad, A. N. Tiwari, Saifullah Khalid, Sandeep Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4888138/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract This research proposes an AI-controlled Unified Power Quality Conditioner (AI-UPQC) to enhance power quality in railway power supply systems. The AI-UPQC utilizes artificial neural networks (ANNs) to generate optimal reference signals for controlling the series and shunt active power filters (APFs). Simulation analysis in a typical 25 kV, 50 Hz traction power supply network demonstrates the effectiveness of the AI-UPQC in maintaining balanced supply voltage and mitigating current harmonics under nonideal operating conditions. The AI-based control strategy outperforms the conventional PI controller in tackling nonlinearity and parameter variations, resulting in superior harmonic mitigation, resonance damping, and dynamic performance. The AI-UPQC significantly reduces voltage and current total harmonic distortion (THD) compared to the uncompensated case and the PI-UPQC. Economic analysis reveals substantial cost savings from reduced equipment maintenance, avoided penalties, and improved energy efficiency. The proposed data-driven AI-UPQC system offers a promising solution to the power quality challenges faced by modern electrified railway transportation networks. Future research directions include advanced machine learning algorithms, real-world testing, scalability, integration with renewable energy sources, and comprehensive economic analysis. Railways UPQC PSO and Pantograph Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Sep, 2024 Reviewers agreed at journal 31 Aug, 2024 Reviews received at journal 31 Aug, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 28 Aug, 2024 Reviews received at journal 24 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers invited by journal 22 Aug, 2024 Editor assigned by journal 13 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 First submitted to journal 09 Aug, 2024 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-4888138","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348154938,"identity":"337dbbe6-fe46-4f9a-87b3-36aef7d82801","order_by":0,"name":"D. K. 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