{"status":"public","_id":"2861","user_id":"245590","citation":{"ieee":"J. Zhang <i>et al.</i>, “Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics,” <i>Energies</i>, vol. 15, no. 18, 2022.","apa":"Zhang, J., Yang, Z., Ding, K., Feng, L., Hamelmann, F., Chen, X., … Chen, L. (2022). Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics. <i>Energies</i>, <i>15</i>(18). <a href=\"https://doi.org/10.3390/en15186567\">https://doi.org/10.3390/en15186567</a>","chicago":"Zhang, Jingwei, Zenan Yang, Kun Ding, Li Feng, Frank Hamelmann, Xihui Chen, Yongjie Liu, and Ling Chen. “Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics.” <i>Energies</i> 15, no. 18 (2022). <a href=\"https://doi.org/10.3390/en15186567\">https://doi.org/10.3390/en15186567</a>.","short":"J. Zhang, Z. Yang, K. Ding, L. Feng, F. Hamelmann, X. Chen, Y. Liu, L. Chen, Energies 15 (2022).","alphadin":"<span style=\"font-variant:small-caps;\">Zhang, Jingwei</span> ; <span style=\"font-variant:small-caps;\">Yang, Zenan</span> ; <span style=\"font-variant:small-caps;\">Ding, Kun</span> ; <span style=\"font-variant:small-caps;\">Feng, Li</span> ; <span style=\"font-variant:small-caps;\">Hamelmann, Frank</span> ; <span style=\"font-variant:small-caps;\">Chen, Xihui</span> ; <span style=\"font-variant:small-caps;\">Liu, Yongjie</span> ; <span style=\"font-variant:small-caps;\">Chen, Ling</span>: Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics. In: <i>Energies</i> Bd. 15, MDPI AG (2022), Nr. 18","bibtex":"@article{Zhang_Yang_Ding_Feng_Hamelmann_Chen_Liu_Chen_2022, title={Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics}, volume={15}, DOI={<a href=\"https://doi.org/10.3390/en15186567\">10.3390/en15186567</a>}, number={186567}, journal={Energies}, publisher={MDPI AG}, author={Zhang, Jingwei and Yang, Zenan and Ding, Kun and Feng, Li and Hamelmann, Frank and Chen, Xihui and Liu, Yongjie and Chen, Ling}, year={2022} }","mla":"Zhang, Jingwei, et al. “Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics.” <i>Energies</i>, vol. 15, no. 18, 6567, MDPI AG, 2022, doi:<a href=\"https://doi.org/10.3390/en15186567\">10.3390/en15186567</a>.","ama":"Zhang J, Yang Z, Ding K, et al. Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics. <i>Energies</i>. 2022;15(18). doi:<a href=\"https://doi.org/10.3390/en15186567\">10.3390/en15186567</a>"},"title":"Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics","date_created":"2023-05-09T09:23:40Z","publication":"Energies","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"year":"2022","publication_status":"published","language":[{"iso":"eng"}],"intvolume":"        15","article_number":"6567","date_updated":"2023-08-29T18:40:24Z","publication_identifier":{"eissn":["1996-1073"]},"abstract":[{"lang":"eng","text":"                  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.\r\n                "}],"type":"journal_article","main_file_link":[{"open_access":"1","url":"https://www.mdpi.com/1996-1073/15/18/6567"}],"quality_controlled":"1","volume":15,"doi":"10.3390/en15186567","author":[{"first_name":"Jingwei","last_name":"Zhang","full_name":"Zhang, Jingwei"},{"last_name":"Yang","full_name":"Yang, Zenan","first_name":"Zenan"},{"last_name":"Ding","full_name":"Ding, Kun","first_name":"Kun"},{"full_name":"Feng, Li","last_name":"Feng","first_name":"Li","id":"237630"},{"orcid":"0000-0001-6141-9874","first_name":"Frank","id":"208487","full_name":"Hamelmann, Frank","last_name":"Hamelmann"},{"full_name":"Chen, Xihui","last_name":"Chen","first_name":"Xihui"},{"first_name":"Yongjie","last_name":"Liu","full_name":"Liu, Yongjie"},{"full_name":"Chen, Ling","last_name":"Chen","first_name":"Ling"}],"issue":"18","publisher":"MDPI AG","oa":"1"}