Abstract
Parameters identification of photovoltaic (PV) models is significant for forecasting power of PV system and simulating the PV models. To accurately identify the parameters of different PV models based on measured current–voltage characteristics, a novel algorithm inspired by triangle vertex and edge, called triangle search optimization (TSO), is proposed in the paper. The TSO algorithm is divided into two phases: the triangle vertex searching (TVS) and triangle edge searching (TES) phases. In the TVS phase, the population is divided into two subpopulations, which can be enhanced by vertex searching operators for exploration and the covariance matrix adaptation evolution strategy (CMA-ES) for exploitation. In the TES phase, the differential evolution vector between superior and inferior solutions is employed to improve the diversity of the population. The experiments on CEC 2017 test suite show that the proposed TSO performs better than the state-of-the-art algorithms in convergence accuracy. The novel algorithm, TSO, is employed to solve the parameters identification problems of single-diode PV model, double-diode PV model and PV module. Comprehensive experiments indicate that TSO can obtain a highly competitive performance compared with other state-of-the-art algorithms, especially in terms of accuracy and robustness.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding in direct support of this work: Aeronautical Science Foundation of China grant 201951096002 and Natural Science Foundation of Shanxi Province grant 2020JQ-481.
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Appendix
Appendix A
All codes used in Sect. 5.1 are obtained from below addresses:
TSO: zhenglei_wei@126.com.
jSO: http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared%20Documents/Forms/AllItems.aspxhttps://ww2.mathworks.cn/matlabcentral/fileexchange/52901-biogeography-based-optimization-bbo
MLSHADE: https://doi.org/10.1016/j.asoc.2020.106527
EBLSHADE: https://sites.google.com/view/optimization-project/files
AEALSCE: wxf825421673@163.com.
HyDE-DF: https://doi.org/10.1145/3319619.3326747
ELSHADE-cnEpSin: http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared%20Documents/Forms/AllItems.aspx
ELSHADE-SPACMA: http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared%20Documents/Forms/AllItems.aspx
Appendix B
All codes used in Sect. 5.2 are obtained from below addresses:
NRO: https://doi.org/10.1109/ACCESS.2019.2918406
IJAYA: http://dx.doi.org/10.1016/j.enconman.2017.08.063
JAYA: ravipudirao@gmail.com.
TLBO: https://www.mathworks.cn/matlabcentral/leexchange/65628-teaching-learning-based-optimization
CSA: https://www.mathworks.cn/matlabcentral/leexchange/56127-crow-search-algorithm
SSA: http://www.alimirjalili.com/index.html
MLSHADE: https://doi.org/10.1016/j.asoc.2020.106527
MLBSA: https://doi.org/10.1016/j.apenergy.2018.06.010.
DE: https://www.mathworks.cn/matlabcentral/leexchange/18593-differential-evolution
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Wei, Z., Zhou, H., Cen, F. et al. A novel evolutionary algorithm inspired from triangle search and its applications on parameters identification of photovoltaic models. Soft Comput 27, 14835–14860 (2023). https://doi.org/10.1007/s00500-023-08575-1
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DOI: https://doi.org/10.1007/s00500-023-08575-1