Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic Systems
This study investigates the performance of different maximum power point tracking (MPPT) methods in a photovoltaic (PV) energy system, focusing on Artificial Neural Networks (ANNs), reinforcement learning (RL), and conventional MPPT approaches. The primary objective is to evaluate the efficiency of...
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MDPI AG
2025-05-01
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| author | Süleyman Emre Eyimaya |
| author_facet | Süleyman Emre Eyimaya |
| author_sort | Süleyman Emre Eyimaya |
| collection | DOAJ |
| description | This study investigates the performance of different maximum power point tracking (MPPT) methods in a photovoltaic (PV) energy system, focusing on Artificial Neural Networks (ANNs), reinforcement learning (RL), and conventional MPPT approaches. The primary objective is to evaluate the efficiency of these methods in maximizing PV energy production under varying environmental conditions, while also analyzing their impact on battery state-of-charge (SOC) and load management. The system is modeled using MATLAB, incorporating real-world climate data from Ankara, including monthly solar irradiance, temperature, and sunlight hours. The ANN-based MPPT employs a multi-layer perceptron (MLP) to improve PV efficiency, while the RL-based MPPT utilizes Q-learning to optimize energy production. A conventional MPPT method serves as a baseline for comparison. Simulations are conducted on an hourly and monthly basis, considering a 7.5 kW PV system with a 20 kWh battery system. The results indicate that both ANN and RL methods outperform the conventional MPPT in terms of annual energy production, with RL achieving the highest efficiency gains. Additionally, the ANN and RL methods demonstrate improved battery SOC management, reducing energy losses. The study concludes that advanced MPPT techniques, particularly RL, offer significant potential for enhancing PV system performance, making them viable solutions for renewable energy optimization. |
| format | Article |
| id | doaj-art-22478bbeedbf4e4ba146a7a90468af97 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-22478bbeedbf4e4ba146a7a90468af972025-08-20T01:56:17ZengMDPI AGApplied Sciences2076-34172025-05-011510558610.3390/app15105586Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic SystemsSüleyman Emre Eyimaya0Department of Electronics and Automation, TUSAS-Kazan Vocational School, Gazi University, 06560 Ankara, TurkeyThis study investigates the performance of different maximum power point tracking (MPPT) methods in a photovoltaic (PV) energy system, focusing on Artificial Neural Networks (ANNs), reinforcement learning (RL), and conventional MPPT approaches. The primary objective is to evaluate the efficiency of these methods in maximizing PV energy production under varying environmental conditions, while also analyzing their impact on battery state-of-charge (SOC) and load management. The system is modeled using MATLAB, incorporating real-world climate data from Ankara, including monthly solar irradiance, temperature, and sunlight hours. The ANN-based MPPT employs a multi-layer perceptron (MLP) to improve PV efficiency, while the RL-based MPPT utilizes Q-learning to optimize energy production. A conventional MPPT method serves as a baseline for comparison. Simulations are conducted on an hourly and monthly basis, considering a 7.5 kW PV system with a 20 kWh battery system. The results indicate that both ANN and RL methods outperform the conventional MPPT in terms of annual energy production, with RL achieving the highest efficiency gains. Additionally, the ANN and RL methods demonstrate improved battery SOC management, reducing energy losses. The study concludes that advanced MPPT techniques, particularly RL, offer significant potential for enhancing PV system performance, making them viable solutions for renewable energy optimization.https://www.mdpi.com/2076-3417/15/10/5586maximum power point trackingreinforcement learningartificial neural networksrenewable energy |
| spellingShingle | Süleyman Emre Eyimaya Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic Systems Applied Sciences maximum power point tracking reinforcement learning artificial neural networks renewable energy |
| title | Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic Systems |
| title_full | Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic Systems |
| title_fullStr | Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic Systems |
| title_full_unstemmed | Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic Systems |
| title_short | Efficiency Analysis of Artificial Intelligence and Conventional Maximum Power Point Tracking Methods in Photovoltaic Systems |
| title_sort | efficiency analysis of artificial intelligence and conventional maximum power point tracking methods in photovoltaic systems |
| topic | maximum power point tracking reinforcement learning artificial neural networks renewable energy |
| url | https://www.mdpi.com/2076-3417/15/10/5586 |
| work_keys_str_mv | AT suleymanemreeyimaya efficiencyanalysisofartificialintelligenceandconventionalmaximumpowerpointtrackingmethodsinphotovoltaicsystems |