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Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics

Author

Listed:
  • Jingwei Zhang

    (College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China)

  • Zenan Yang

    (College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China)

  • Kun Ding

    (College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China)

  • Li Feng

    (Solar Computing Laboratory, University of Applied Sciences Bielefeld, Artilleriestraße 9, 32427 Minden, Germany)

  • Frank Hamelmann

    (Solar Computing Laboratory, University of Applied Sciences Bielefeld, Artilleriestraße 9, 32427 Minden, Germany)

  • Xihui Chen

    (College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China)

  • Yongjie Liu

    (Engineering Research Center of Dredging Technology of Ministry of Education, Changzhou 213022, China)

  • Ling Chen

    (School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huai’an 223300, China)

Abstract

Currently, the accuracy of modeling a photovoltaic (PV) array for fault diagnosis is still unsatisfactory due to the fact that the modeling accuracy is limited by the accuracy of extracted model parameters. In this paper, the modeling of a PV array based on multi-agent deep reinforcement learning (RL) using the residuals of I–V characteristics is proposed. The environment state based on the high dimensional residuals of I–V characteristics and the corresponding cooperative reward is presented for the RL agents. The actions of each agent considering the damping amplitude are designed. Then, the entire framework of modeling a PV array based on multi-agent deep RL is presented. The feasibility and accuracy of the proposed method are verified by the one-year measured data of a PV array. The experimental results show that the higher modeling accuracy of the next time step is obtained by the extracted model parameters using the proposed method, compared with that using the conventional meta-heuristic algorithms and the analytical method. The daily root mean square error (RMSE) is approximately 0.5015 A on the first day, and converges to 0.1448 A on the last day of training. The proposed multi-agent deep RL framework simplifies the design of states and rewards for extracting model parameters.

Suggested Citation

  • Jingwei Zhang & Zenan Yang & Kun Ding & Li Feng & Frank Hamelmann & Xihui Chen & Yongjie Liu & Ling Chen, 2022. "Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics," Energies, MDPI, vol. 15(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6567-:d:909830
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    1. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    2. Fouzi Harrou & Ying Sun & Bilal Taghezouit & Abdelkader Dairi, 2023. "Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting," Energies, MDPI, vol. 16(18), pages 1-5, September.

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