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An Improved Maximum Power Point Tracking Control Scheme for Photovoltaic Systems: Integrating Sparrow Search Algorithm-Optimized Support Vector Regression and Optimal Regulation for Enhancing Precision and Robustness

Author

Listed:
  • Mingjun He

    (Electric Power Scientific Research Institute of Guizhou Power Grid Co., Guiyang 550001, China)

  • Ke Zhou

    (Guizhou Chuangxing Electric Power Scientific Research Institute Co., Guiyang 550001, China)

  • Yutao Xu

    (Electric Power Scientific Research Institute of Guizhou Power Grid Co., Guiyang 550001, China)

  • Jinsong Yu

    (Liupanshui Power Supply Bureau of Guizhou Power Grid Co., Ltd., Liupanshui 553001, China)

  • Yangquan Qu

    (Liupanshui Power Supply Bureau of Guizhou Power Grid Co., Ltd., Liupanshui 553001, China)

  • Xiankui Wen

    (Electric Power Scientific Research Institute of Guizhou Power Grid Co., Guiyang 550001, China)

Abstract

Overdependence on fossil fuels contributes to global warming and environmental degradation. Solar energy, particularly photovoltaic (PV) power generation, has emerged as a widely adopted clean and renewable alternative. To increase and enhance the efficiency of PV systems, maximum power point tracking (MPPT) technology is essential. However, achieving accurate tracking control while balancing overall performance in terms of stability, dynamic response, and robustness remains a challenge. In this study, an improved MPPT control scheme based on the technique of predicting the reference current at the MPP and regulating the optimal current is proposed. Support vector regression (SVR) endowed with a strong generalization stability was adopted to model the nonlinear relationship between the PV output current and the environmental factors of irradiance and temperature. The sparrow search algorithm (SSA), recognized for its excellent global search capability, was employed to optimize the hyperparameters of SVR to further increase the prediction accuracy. To satisfy the performance requirements for the current-tracking process, a linear quadratic (LQ) optimal control strategy was applied to design the current regulator based on the PV system’s state-space model. The effectiveness and superior performance of the suggested SSA-SVR-LQ control scheme were validated using measured data under real operating conditions.

Suggested Citation

  • Mingjun He & Ke Zhou & Yutao Xu & Jinsong Yu & Yangquan Qu & Xiankui Wen, 2025. "An Improved Maximum Power Point Tracking Control Scheme for Photovoltaic Systems: Integrating Sparrow Search Algorithm-Optimized Support Vector Regression and Optimal Regulation for Enhancing Precisio," Energies, MDPI, vol. 18(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3182-:d:1681185
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    References listed on IDEAS

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    1. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
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