AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization
Abstract This research paper presents an advanced AI-driven hybrid power quality management system for electrical railways that addresses critical challenges in 25 kV AC traction networks through a novel integration of single-phase PV-UPQC with ANN-Lyapunov control architecture. The system effective...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85393-5 |
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| author | D. K. Nishad A. N. Tiwari Saifullah Khalid Sandeep Gupta Anand Shukla |
| author_facet | D. K. Nishad A. N. Tiwari Saifullah Khalid Sandeep Gupta Anand Shukla |
| author_sort | D. K. Nishad |
| collection | DOAJ |
| description | Abstract This research paper presents an advanced AI-driven hybrid power quality management system for electrical railways that addresses critical challenges in 25 kV AC traction networks through a novel integration of single-phase PV-UPQC with ANN-Lyapunov control architecture. The system effectively manages voltage unbalance exceeding 2%, high THD, voltage variations of ± 10%, and poor power factor through a dual-approach methodology combining ANN-based reference signal generation with Lyapunov optimization, enabling dynamic parameter tuning and real-time load adaptation. MATLAB/Simulink simulations validate the system’s superior performance, demonstrating significant improvements, including voltage unbalance reduction from 1.5 to 0.8%, THD reduction below 1%, unity power factor correction, 40% faster dynamic response, and DC link voltage regulation within ± 2%, while maintaining 95% overall system efficiency. Integrating ANN-based shunt and series APF control, Lyapunov optimization, and PV integration establishes a robust framework for enhanced energy efficiency and power quality management in modern railway systems. |
| format | Article |
| id | doaj-art-56034c80cdff4faa8dea93914bfd7ea3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-56034c80cdff4faa8dea93914bfd7ea32025-01-26T12:32:20ZengNature PortfolioScientific Reports2045-23222025-01-0115113010.1038/s41598-025-85393-5AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimizationD. K. Nishad0A. N. Tiwari1Saifullah Khalid2Sandeep Gupta3Anand Shukla4Department of Electrical Engineering, M. M. M. U. TDepartment of Electrical Engineering, M. M. M. U. TAirport Authority of IndiaElectrical Engineering, Graphic Era (Deemed to be University)Wollega UniversityAbstract This research paper presents an advanced AI-driven hybrid power quality management system for electrical railways that addresses critical challenges in 25 kV AC traction networks through a novel integration of single-phase PV-UPQC with ANN-Lyapunov control architecture. The system effectively manages voltage unbalance exceeding 2%, high THD, voltage variations of ± 10%, and poor power factor through a dual-approach methodology combining ANN-based reference signal generation with Lyapunov optimization, enabling dynamic parameter tuning and real-time load adaptation. MATLAB/Simulink simulations validate the system’s superior performance, demonstrating significant improvements, including voltage unbalance reduction from 1.5 to 0.8%, THD reduction below 1%, unity power factor correction, 40% faster dynamic response, and DC link voltage regulation within ± 2%, while maintaining 95% overall system efficiency. Integrating ANN-based shunt and series APF control, Lyapunov optimization, and PV integration establishes a robust framework for enhanced energy efficiency and power quality management in modern railway systems.https://doi.org/10.1038/s41598-025-85393-5Power QualityArtificial neural networksLyapunov ControlPhotovoltaic integrationUnified Power Quality Conditioner |
| spellingShingle | D. K. Nishad A. N. Tiwari Saifullah Khalid Sandeep Gupta Anand Shukla AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization Scientific Reports Power Quality Artificial neural networks Lyapunov Control Photovoltaic integration Unified Power Quality Conditioner |
| title | AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization |
| title_full | AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization |
| title_fullStr | AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization |
| title_full_unstemmed | AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization |
| title_short | AI-based hybrid power quality control system for electrical railway using single phase PV-UPQC with Lyapunov optimization |
| title_sort | ai based hybrid power quality control system for electrical railway using single phase pv upqc with lyapunov optimization |
| topic | Power Quality Artificial neural networks Lyapunov Control Photovoltaic integration Unified Power Quality Conditioner |
| url | https://doi.org/10.1038/s41598-025-85393-5 |
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