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• W4297179417 abstract "The rapid and accurate acquisition of model parameters of photovoltaic (PV) modules is of great significance for the efficient operation and maintenance of photovoltaic power plants under the background of the development of new power systems. To solve the problems of poor accuracy and slow velocity of identification of traditional PV modules model parameters, this paper proposed an identification of parameter method based on fuzzy adaptive differential evolution algorithm (FADE). In the proposed method, based on the I–V output characteristics of PV modules, a DE/current-to-SP-best/1 variation strategy is constructed to increase the local search capability of module model parameter identification; In addition, fuzzy selection strategy and an adaptive parameter adjustment strategy are introduced to effectively control the crossover probability and mutation factors to avoid the discrimination into local optimum while improving the convergence of the algorithm. The performance of the proposed method has been verified by extracting classical polycrystalline and monocrystalline modules parameters, The solution results of the polycrystalline module Photowatt-PWP201 (2.42507E−3), STP6-120/36 (1.66006E−2) and monocrystalline module STM6-40/36 (1.72981E−3) comprehensively show that FADE has better accuracy and robustness compared with other algorithms." @default.
• W4297179417 created "2022-09-27" @default.
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• W4297179417 date "2022-11-01" @default.
• W4297179417 modified "2023-10-12" @default.
• W4297179417 title "Research on the parameter identification of PV module based on fuzzy adaptive differential evolution algorithm" @default.
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• W4297179417 doi "https://doi.org/10.1016/j.egyr.2022.09.057" @default.
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