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Optimizing PID control for maximum power point tracking in photovoltaic systems under variable and partial shading conditions

Author

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  • Karuppasamy, C.
  • Senthil Kumar, C.
  • Ganesan, R.
  • Elamparithi, P.

Abstract

The development of MPPT algorithms is crucial for optimizing energy utilization in photovoltaic systems. This manuscript introduces a hybrid method to enhance MPPT performance under partial shading conditions by optimizing PID control. The proposed AOA-DWNN method integrates the Aquila Optimization Algorithm (AOA) for tuning PID gain settings and the Dynamic Wavelet Neural Network (DWNN) for predicting optimal converter parameters. This combination improves power tracking accuracy and optimizes energy utilization in varying environmental conditions. The method is put into practice in MATLAB and compared with existing methods, including the Salp Swarm Algorithm and Sine Cosine Algorithm (SSA-SCA), Firefly Algorithm and Particle Swarm Optimization (FA-PSO), and Artificial Bees Colony and Cuckoo Search Algorithm (ABC-CSA). The results demonstrate a significantly lower error rate of 0.89 %, compared to 1.5 %, 1.2 %, and 1 % in existing approaches. Additionally, the proposed technique achieves an efficiency of 99.96 %, surpassing the 98.94 %, 97.95 %, and 96.94 % of other methods. The findings highlight the effectiveness of the AOA-DWNN technique in improving photovoltaic system performance, ensuring more accurate and reliable MPPT operation under partial shading conditions.

Suggested Citation

  • Karuppasamy, C. & Senthil Kumar, C. & Ganesan, R. & Elamparithi, P., 2025. "Optimizing PID control for maximum power point tracking in photovoltaic systems under variable and partial shading conditions," Renewable Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:renene:v:246:y:2025:i:c:s0960148125005920
    DOI: 10.1016/j.renene.2025.122930
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    References listed on IDEAS

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    1. Zhang, Xiaoshun & Li, Shengnan & He, Tingyi & Yang, Bo & Yu, Tao & Li, Haofei & Jiang, Lin & Sun, Liming, 2019. "Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition," Energy, Elsevier, vol. 174(C), pages 1079-1090.
    2. Mostafa Bakkar & Ahmed Aboelhassan & Mostafa Abdelgeliel & Michael Galea, 2021. "PV Systems Control Using Fuzzy Logic Controller Employing Dynamic Safety Margin under Normal and Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-20, February.
    3. Celikel, Resat & Yilmaz, Musa & Gundogdu, Ahmet, 2022. "A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions," Renewable Energy, Elsevier, vol. 184(C), pages 361-373.
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    1. Md Shamim Hossain, Wang Lu, Li Libo, Conteh Alimamy, Ge Qiang*, 2025. "Optimization of MPPT in PV Systems Using Machine Learning Under Partial Shading Conditions," International Journal of Innovations in Science & Technology, 50sea, vol. 7(7), pages 338-354, May.
    2. Kangji Li & Jialu Shi & Chenglei Hu & Wenping Xue, 2025. "The Intelligentization Process of Agricultural Greenhouse: A Review of Control Strategies and Modeling Techniques," Agriculture, MDPI, vol. 15(20), pages 1-29, October.

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