Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms
The integration of renewable energy sources such as photovoltaic (PV) and wind systems demands high-efficiency, high-voltage gain power conversion architectures. However, interleaved boost converters, while suitable for such applications, face challenges in balancing complexity, scalability, cost, a...
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Elsevier
2025-06-01
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| author | G.Veera Sankara Reddy S. Vijayaraj |
| author_facet | G.Veera Sankara Reddy S. Vijayaraj |
| author_sort | G.Veera Sankara Reddy |
| collection | DOAJ |
| description | The integration of renewable energy sources such as photovoltaic (PV) and wind systems demands high-efficiency, high-voltage gain power conversion architectures. However, interleaved boost converters, while suitable for such applications, face challenges in balancing complexity, scalability, cost, and dynamic environmental variability. This study introduces a series of novel intelligent control frameworks to overcome these limitations and improve overall system performance. Firstly, a Neuro-LSTM BitterSec Optimization Network (NL-BSONet) is proposed to enhance the efficiency of high-voltage gain interleaved boost converters while minimizing system complexity. This hybrid approach leverages neural networks and LSTM-based learning for real-time optimization, offering improved scalability and lower switching losses. To address power quality issues caused by fluctuating irradiance and wind speeds, the study introduces the Adaptive Neuro-Deep Reinforcement Learning Bitterling Optimizer (AN-DRLBO). This model integrates Deep Reinforcement Learning (DRL) for adaptive energy conversion, Adaptive Neural Networks (ANN) for real-time system stabilization, and Bitterling Fish Optimization (BFO) for robust performance under transient conditions. Furthermore, due to the difficulty in achieving optimal control parameters under variable environmental conditions, an Adaptive LSTM-Encoded Secretary Optimization Network (AL-SONet) is developed. This framework employs Long Short-Term Memory (LSTM) networks for predictive control, Autoencoder-based Optimization (AEO) for feature extraction and simplification, and Secretary Bird Optimization (SBO) for dynamic parameter tuning. The proposed architectures demonstrate superior performance, achieving 97 % energy conversion efficiency, a voltage gain of 32.5 dB, and minimal output ripple, thereby ensuring stable and efficient integration of renewable energy sources. This research contributes a comprehensive and adaptive control solution for next-generation renewable energy systems. |
| format | Article |
| id | doaj-art-b463a89b9077446fa79a8d30306c15a7 |
| institution | OA Journals |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-b463a89b9077446fa79a8d30306c15a72025-08-20T02:35:34ZengElsevierFranklin Open2773-18632025-06-011110029110.1016/j.fraope.2025.100291Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithmsG.Veera Sankara Reddy0S. Vijayaraj1Corresponding author.; Department of EEE, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, IndiaDepartment of EEE, Vels Institute of Science, Technology & Advanced Studies, Chennai, Tamil Nadu, IndiaThe integration of renewable energy sources such as photovoltaic (PV) and wind systems demands high-efficiency, high-voltage gain power conversion architectures. However, interleaved boost converters, while suitable for such applications, face challenges in balancing complexity, scalability, cost, and dynamic environmental variability. This study introduces a series of novel intelligent control frameworks to overcome these limitations and improve overall system performance. Firstly, a Neuro-LSTM BitterSec Optimization Network (NL-BSONet) is proposed to enhance the efficiency of high-voltage gain interleaved boost converters while minimizing system complexity. This hybrid approach leverages neural networks and LSTM-based learning for real-time optimization, offering improved scalability and lower switching losses. To address power quality issues caused by fluctuating irradiance and wind speeds, the study introduces the Adaptive Neuro-Deep Reinforcement Learning Bitterling Optimizer (AN-DRLBO). This model integrates Deep Reinforcement Learning (DRL) for adaptive energy conversion, Adaptive Neural Networks (ANN) for real-time system stabilization, and Bitterling Fish Optimization (BFO) for robust performance under transient conditions. Furthermore, due to the difficulty in achieving optimal control parameters under variable environmental conditions, an Adaptive LSTM-Encoded Secretary Optimization Network (AL-SONet) is developed. This framework employs Long Short-Term Memory (LSTM) networks for predictive control, Autoencoder-based Optimization (AEO) for feature extraction and simplification, and Secretary Bird Optimization (SBO) for dynamic parameter tuning. The proposed architectures demonstrate superior performance, achieving 97 % energy conversion efficiency, a voltage gain of 32.5 dB, and minimal output ripple, thereby ensuring stable and efficient integration of renewable energy sources. This research contributes a comprehensive and adaptive control solution for next-generation renewable energy systems.http://www.sciencedirect.com/science/article/pii/S2773186325000817Renewable energy integrationHigh-voltage gain convertersDynamic adaptationReal-time optimizationStability enhancementEfficiency maximization |
| spellingShingle | G.Veera Sankara Reddy S. Vijayaraj Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms Franklin Open Renewable energy integration High-voltage gain converters Dynamic adaptation Real-time optimization Stability enhancement Efficiency maximization |
| title | Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms |
| title_full | Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms |
| title_fullStr | Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms |
| title_full_unstemmed | Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms |
| title_short | Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms |
| title_sort | optimizing high voltage gain interleaved boost converters for pv and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms |
| topic | Renewable energy integration High-voltage gain converters Dynamic adaptation Real-time optimization Stability enhancement Efficiency maximization |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000817 |
| work_keys_str_mv | AT gveerasankarareddy optimizinghighvoltagegaininterleavedboostconvertersforpvandwindsystemsusinghybriddeeplearningwithbitterlingfishandsecretarybirdalgorithms AT svijayaraj optimizinghighvoltagegaininterleavedboostconvertersforpvandwindsystemsusinghybriddeeplearningwithbitterlingfishandsecretarybirdalgorithms |