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Multi-strategy adaptive guidance differential evolution algorithm using fitness-distance balance and opposition-based learning for constrained global optimization of photovoltaic cells and modules

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  • Liu, Qianlong
  • Zhang, Chu
  • Li, Zhengbo
  • Peng, Tian
  • Zhang, Zhao
  • Du, Dongsheng
  • Nazir, Muhammad Shahzad

Abstract

It is of great significance to obtain the parameters of photovoltaic (PV) models quickly and accurately for the efficient operation and maintenance of PV power plants. A multi-strategy adaptive guidance differential evolution (AGDE) algorithm using fitness-distance balance (FDB) and opposition-based learning (OBL) is proposed for constrained global optimization of PV cells and modules. FDB and OBL are added on the basis of AGDE to improve the local search ability of the algorithm, and thus identify the parameters of the PV models faster and more accurately. Among the two improved strategies, FDB is a recently developed powerful method that can efficiently model selection processes in nature. In this study, the mutation mating pool of the AGDE algorithm is redesigned using the FDB method. OBL is adopted to increases the initial population diversity of AGDE. In order to verify the performance of the proposed FDB-AGDE in the parameter estimation for PV models, the experimental verification is carried out on two PV cells and three PV modules. The experimental results show that FDB-OADE has better accuracy and robustness in photovoltaic identification compared with other algorithms.

Suggested Citation

  • Liu, Qianlong & Zhang, Chu & Li, Zhengbo & Peng, Tian & Zhang, Zhao & Du, Dongsheng & Nazir, Muhammad Shahzad, 2024. "Multi-strategy adaptive guidance differential evolution algorithm using fitness-distance balance and opposition-based learning for constrained global optimization of photovoltaic cells and modules," Applied Energy, Elsevier, vol. 353(PA).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s030626192301396x
    DOI: 10.1016/j.apenergy.2023.122032
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    1. Abbassi, Rabeh & Abbassi, Abdelkader & Jemli, Mohamed & Chebbi, Souad, 2018. "Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 453-474.
    2. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    3. Wang, Meng & Peng, Jinqing & Luo, Yimo & Shen, Zhicheng & Yang, Hongxing, 2021. "Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements," Energy, Elsevier, vol. 224(C).
    4. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    5. Zhang, Chu & Ji, Chunlei & Hua, Lei & Ma, Huixin & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction," Renewable Energy, Elsevier, vol. 197(C), pages 668-682.
    6. Wu, Han & Yuan, Yue & Zhang, Xinsong & Miao, Ankang & Zhu, Junpeng, 2022. "Robust comprehensive PV hosting capacity assessment model for active distribution networks with spatiotemporal correlation," Applied Energy, Elsevier, vol. 323(C).
    7. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    8. Liu, Guanjun & Wang, Yun & Qin, Hui & Shen, Keyan & Liu, Shuai & Shen, Qin & Qu, Yuhua & Zhou, Jianzhong, 2023. "Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method," Renewable Energy, Elsevier, vol. 209(C), pages 231-247.
    9. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    10. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    11. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    12. Chen, Xu & Yu, Kunjie & Du, Wenli & Zhao, Wenxiang & Liu, Guohai, 2016. "Parameters identification of solar cell models using generalized oppositional teaching learning based optimization," Energy, Elsevier, vol. 99(C), pages 170-180.
    Full references (including those not matched with items on IDEAS)

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