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Parida, Sritam, Patnaik, Bhabani, Mohan, G, Maniraj, P, Kaliappan, S, Behera, Maheswar, Sahu, Manoj, Tripathy, Omkar, Sahu, Supriya, GANTHIA, BIBHU PRASAD. (1405). Hybrid Deep Reinforcement Learning with Leaky LMS-ANN for Active Power Filter-Based UPQC in PV-Integrated System. , (), -. doi: 10.22059/jser.2026.404115.1649
Sritam Parida; Bhabani Patnaik; G Mohan; P Maniraj; S Kaliappan; Maheswar Prasad Behera; Manoj Kumar Sahu; Omkar Tripathy; Supriya Sahu; BIBHU PRASAD GANTHIA. "Hybrid Deep Reinforcement Learning with Leaky LMS-ANN for Active Power Filter-Based UPQC in PV-Integrated System". , , , 1405, -. doi: 10.22059/jser.2026.404115.1649
Parida, Sritam, Patnaik, Bhabani, Mohan, G, Maniraj, P, Kaliappan, S, Behera, Maheswar, Sahu, Manoj, Tripathy, Omkar, Sahu, Supriya, GANTHIA, BIBHU PRASAD. (1405). 'Hybrid Deep Reinforcement Learning with Leaky LMS-ANN for Active Power Filter-Based UPQC in PV-Integrated System', , (), pp. -. doi: 10.22059/jser.2026.404115.1649
Parida, Sritam, Patnaik, Bhabani, Mohan, G, Maniraj, P, Kaliappan, S, Behera, Maheswar, Sahu, Manoj, Tripathy, Omkar, Sahu, Supriya, GANTHIA, BIBHU PRASAD. Hybrid Deep Reinforcement Learning with Leaky LMS-ANN for Active Power Filter-Based UPQC in PV-Integrated System. , 1405; (): -. doi: 10.22059/jser.2026.404115.1649

Hybrid Deep Reinforcement Learning with Leaky LMS-ANN for Active Power Filter-Based UPQC in PV-Integrated System

Journal of Solar Energy Research
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 29 فروردین 1405
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22059/jser.2026.404115.1649
نویسندگان
Sritam Parida1؛ Bhabani Patnaik2؛ G Mohan3؛ P Maniraj4؛ S Kaliappan5؛ Maheswar Prasad Behera6؛ Manoj Kumar Sahu7؛ Omkar Tripathy8؛ Supriya Sahu9؛ BIBHU PRASAD GANTHIA* 6
1Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, odisha, India
2Electrical and Electronics engineering, GIFT Autonomous Bhubaneswar
3Associate Professor Department of Mathematics, K.S.Rangasamy College of Technology, Tiruchengode
4Department of Electrical and Electronics Engineering, Thalavapalayam, Karur, Tamilnadu - 639 113
5Electrical and Electronics Engineering, Kumaraguru College of Technology, Saravanampatti, Coimbatore, India
6Assistant Professor, Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, odisha, India
7Department of Electrical Engineering, Centre for Advanced Post Graduate Studies, Biju Patnaik University of Technology, Rourkela, Odisha, India
8Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, BPUT, Rourkela, Odisha, India.
9Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
چکیده
The growing integration of photovoltaic (PV) systems into conventional distribution networks has intensified major power quality challenges such as 18–25% harmonic distortion, power factor deviation between 0.92 and 0.95, frequent voltage fluctuations, and load imbalance exceeding 8%. This study proposes a Hybrid Deep Reinforcement Learning (DRL) with Leaky LMS-ANN controlled Unified Power Quality Conditioner (UPQC) to achieve enhanced active power filtering in PV-integrated systems. The hybrid controller leverages the decision-making capability of DRL alongside the adaptive parameter tuning of the Leaky LMS-ANN algorithm, enabling real-time optimization under variable irradiance, nonlinear loads, and load-switching conditions. MATLAB/Simulink validation demonstrates substantial performance gains: total harmonic distortion is reduced from 18.4% to 2.97% (84% reduction), reactive power compensation improves by 55.6%, and voltage imbalance declines from 8.5% to 1.2%. The DC-link voltage stability increases by 23%, while the power factor is maintained near unity at 0.998. Compared with conventional ANN, LMS, and MPC controllers, the proposed approach delivers superior harmonic mitigation, voltage regulation, and system reliability, making it well suited for advanced smart grid applications.
کلیدواژه‌ها
Hybrid DRL؛ Photovoltaic Integration؛ Leaky LMS-ANN؛ Power Quality؛ UPQC؛ THD
آمار
تعداد مشاهده مقاله: 291





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