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Photovoltaic Panel Parameter Estimation Enhancement Using a Modified Quasi-Opposition-Based Killer Whale Optimization Technique

Author

Listed:
  • Cilina Touabi

    (Laboratory of Signals and Systems, Institute of Electrical Engineering and Electronics, University M’Hamed Bougara, Boumerdes 35000, Algeria)

  • Abderrahmane Ouadi

    (Laboratory of Signals and Systems, Institute of Electrical Engineering and Electronics, University M’Hamed Bougara, Boumerdes 35000, Algeria)

  • Hamid Bentarzi

    (Laboratory of Signals and Systems, Institute of Electrical Engineering and Electronics, University M’Hamed Bougara, Boumerdes 35000, Algeria)

  • Abdelmadjid Recioui

    (Laboratory of Signals and Systems, Institute of Electrical Engineering and Electronics, University M’Hamed Bougara, Boumerdes 35000, Algeria)

Abstract

Photovoltaic (PV) energy generation has seen rapid growth in recent years due to its sustainability and environmental benefits. However, accurately identifying PV panel parameters is crucial for enhancing system performance, especially under varying environmental conditions. This study presents an enhanced approach for estimating PV panel parameters using a Modified Quasi-Opposition-Based Killer Whale Optimization (MQOB-KWO) technique. The research aims to improve parameter extraction accuracy by optimizing the one-diode model (ODM), a widely used representation of PV cells, using a modified metaheuristic optimization technique. The proposed algorithm leverages a Quasi-Opposition-Based Learning (QOBL) mechanism to enhance search efficiency and convergence speed. The methodology involves implementing the MQOB-KWO in MATLAB R2021a and evaluating its effectiveness through experimental I-V data from two unlike photovoltaic panels. The findings are contrasted to established optimization techniques from the literature, such as the original Killer Whale Optimization (KWO), Improved Opposition-Based Particle Swarm Optimization (IOB-PSO), Improved Cuckoo Search Algorithm (ImCSA), and Chaotic Improved Artificial Bee Colony (CIABC). The findings demonstrate that the proposed MQOB-KWO achieves superior accuracy with the lowest Root Mean Square Error (RMSE) compared to other methods, and the lowest error rates (Root Mean Square Error—RMSE, and Integral Absolute Error—IAE) compared to the original KWO, resulting in a better value of the coefficient of determination ( R 2 ), hence effectively capturing PV module characteristics. Additionally, the algorithm shows fast convergence, making it suitable for real-time PV system modeling. The study confirms that the proposed optimization technique is a reliable and efficient tool for improving PV parameter estimation, contributing to better system efficiency and operational performance.

Suggested Citation

  • Cilina Touabi & Abderrahmane Ouadi & Hamid Bentarzi & Abdelmadjid Recioui, 2025. "Photovoltaic Panel Parameter Estimation Enhancement Using a Modified Quasi-Opposition-Based Killer Whale Optimization Technique," Sustainability, MDPI, vol. 17(11), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5161-:d:1671696
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    References listed on IDEAS

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    1. Tong Kang & Jiangang Yao & Min Jin & Shengjie Yang & ThanhLong Duong, 2018. "A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models," Energies, MDPI, vol. 11(5), pages 1-31, April.
    2. Diego Oliva & Ahmed A. Ewees & Mohamed Abd El Aziz & Aboul Ella Hassanien & Marco Peréz-Cisneros, 2017. "A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells," Energies, MDPI, vol. 10(7), pages 1-19, June.
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