Information sharing search boosted whale optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

被引:39
作者
Peng, Lemin [1 ]
He, Caitou [1 ]
Heidari, Ali Asghar [1 ]
Zhang, Qian [2 ]
Chen, Huiling [1 ]
Liang, Guoxi [3 ]
Aljehane, Nojood O. [4 ]
Mansour, Romany F. [5 ]
机构
[1] Wenzhou Univ, Dept Comp Sci & Artificial Intelligence, Wenzhou 325035, Peoples R China
[2] Wenzhou Univ Technol, Sch Data Sci & Artificial Intelligence, Wenzhou 325035, Zhejiang, Peoples R China
[3] Dept Informat Technol, Wenzhou Polytech, Wenzhou 325035, Peoples R China
[4] Univ Tabuk, Fac Comp & Informat Technol, Tabuk, Saudi Arabia
[5] New Valley Univ, Fac Sci, Dept Math, El Kharga 72511, Egypt
关键词
Whale optimization algorithm; Optimization; Swarm; -intelligence; Parameter estimation; Photovoltaic models; Solar energy; PARTICLE SWARM OPTIMIZATION; SINE COSINE ALGORITHM; GLOBAL OPTIMIZATION; INSPIRED OPTIMIZER; GENETIC ALGORITHM; IDENTIFICATION; CELL; EVOLUTIONARY; EFFICIENT; RECOMMENDATION;
D O I
10.1016/j.enconman.2022.116246
中图分类号
O414.1 [热力学];
学科分类号
摘要
With the recent emphasis on new energy sources, solar photovoltaic cells have received widespread attention from scholars as a highly efficient and clean new energy source. Researchers model solar photovoltaic cells and analyze the values of various unknown parameters to improve their efficiency in converting solar energy into electric energy. To extract unknown parameters more accurately and efficiently, we propose a whale optimi-zation algorithm (WOA) driven by the information sharing search mechanism and the Nelder-Mead simplex (NMs) mechanism, which ISNMWOA. The WOA can perform a global search of the parameters of the photo-voltaic model to obtain a number of initial vectors of model parameters that satisfy the parameter range. The information sharing search strategy performs a further coarse local search on a number of feasible solutions generated. In contrast, the NMs can perform a further refined local search in the vicinity of the optimal pa-rameters to obtain the final vector of optimally performing model parameters. First, we tested ISNMWOA against six advanced algorithms in CEC 2014. We then used the ISNMWOA to evaluate the parameters of the solar cells and PV modules. The RMSE of ISNMWOA on single diode model (SDM), double diode model (DDM), three diode model (TDM), and photovoltaic module model (PV) are 9.8602E-04, 9.8248E-04, 9.8248E-04 and 2.4251E-03 respectively. Compared to the original WOA, the RMSE of ISNMWOA on SDM, DDM, TDM, and PV decreased by 30.01%, 24.24%, 50.98%, and 6.94%, respectively. Moreover, compared to the high-performing GOFPANM, ISNMWOA runs 298 times faster than GOFPANM with almost identical optimization results. We evaluated the performance of three commercial PV modules under different irradiance and temperature conditions using ISNMWOA to verify the reliability of the experiment. The experimental results show that the ISNMWOA out-performs similar algorithms currently available.
引用
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页数:27
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