2022
DOI: 10.1016/j.egyr.2022.01.008
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A modified stochastic fractal search algorithm for parameter estimation of solar cells and PV modules

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Cited by 16 publications
(6 citation statements)
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“…Although the above solution techniques have achieved varying degrees of success, the difficulty in parameter setting of these methods and the lack of design considerations for some optimization problems make them show different degrees of optimization deficiency in finding the optimal solution. [19][20][21][22] Thankfully, a series of heuristic algorithms with advanced theories have been successively proposed, including monarch butterfly optimization, 23 moth search algorithm, 24 hunger games search (HGS), 25 Runge-Kutta method, 26 colony predation algorithm, 27 weighted mean of vectors, 28 Harris Hawks Optimization (HHO), 29 rime optimization algorithm (RIME), 30 and the sine-cosine algorithm (SCA). 31 In addition, a number of combinatorial algorithms have been recognized, such as BOAALO 32 based on butterfly optimization algorithm, 33 and ant lion optimizer 34 ; CNNA-BES 35 based on convolutional neural network architecture and bald eagle search 36 optimization algorithm; QGBWOA 37 based on quasiopposition-based learning and Gaussian barebone mechanism; MOQBHHO 38 based on K-Nearest Neighbor method and multiobjective HHO.…”
Section: Introductionmentioning
confidence: 99%
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“…Although the above solution techniques have achieved varying degrees of success, the difficulty in parameter setting of these methods and the lack of design considerations for some optimization problems make them show different degrees of optimization deficiency in finding the optimal solution. [19][20][21][22] Thankfully, a series of heuristic algorithms with advanced theories have been successively proposed, including monarch butterfly optimization, 23 moth search algorithm, 24 hunger games search (HGS), 25 Runge-Kutta method, 26 colony predation algorithm, 27 weighted mean of vectors, 28 Harris Hawks Optimization (HHO), 29 rime optimization algorithm (RIME), 30 and the sine-cosine algorithm (SCA). 31 In addition, a number of combinatorial algorithms have been recognized, such as BOAALO 32 based on butterfly optimization algorithm, 33 and ant lion optimizer 34 ; CNNA-BES 35 based on convolutional neural network architecture and bald eagle search 36 optimization algorithm; QGBWOA 37 based on quasiopposition-based learning and Gaussian barebone mechanism; MOQBHHO 38 based on K-Nearest Neighbor method and multiobjective HHO.…”
Section: Introductionmentioning
confidence: 99%
“…Although the above solution techniques have achieved varying degrees of success, the difficulty in parameter setting of these methods and the lack of design considerations for some optimization problems make them show different degrees of optimization deficiency in finding the optimal solution 19–22 . Thankfully, a series of heuristic algorithms with advanced theories have been successively proposed, including monarch butterfly optimization, 23 moth search algorithm, 24 hunger games search (HGS), 25 Runge–Kutta method, 26 colony predation algorithm, 27 weighted mean of vectors, 28 Harris Hawks Optimization (HHO), 29 rime optimization algorithm (RIME), 30 and the sine–cosine algorithm (SCA) 31 .…”
Section: Introductionmentioning
confidence: 99%
“…A detailed literature consisting of 30 valuable papers, including meta‐heuristic algorithms, is given in Table 1 . Based on the literature review, namely, the INFO algorithm, [ 14 ] atomic orbital search algorithm, [ 15 ] improved electromagnetism‐like mechanism algorithm, [ 16 ] northern goshawk optimization algorithm, [ 17 ] improved queuing search optimization algorithm based on differential evaluation, [ 18 ] fractional Henon chaotic Harris Hawks optimization, [ 19 ] hunter‐prey algorithm, [ 20 ] robust niching optimization, [ 21 ] wild horse optimizer, [ 20 ] heap‐based optimizer, [ 22 ] modified stochastic fractal search algorithm, [ 23 ] circle search algorithm, [ 24 ] orthogonal learning gradient‐based optimization, [ 25 ] improved rao‐1 algorithm, [ 26 ] improved political optimization algorithm, [ 27 ] memory‐based improved gorilla troops optimizer, [ 28 ] adaptive fractional‐order Archimedes optimization algorithm, [ 29 ] multistrategy cuckoo search algorithm, [ 30 ] whale optimizer with Nelder‐Mead simplex, [ 31 ] Runge‐Kutta optimizer, [ 32 ] turbulent flow of water‐based optimization, [ 33 ] supply demand optimization, [ 34 ] enhanced chaotic JAYA algorithm, [ 35 ] modified teaching–learning based optimization, [ 36 ] enhanced marine predators algorithm, [ 37 ] hybrid African vultures–grey wolf optimizer, [ 38 ] niche particle swarm optimization in parallel computing, [ 39 ] simulated annealing optimization, [ 40 ] enhanced ant lion optimizer, [ 41 ] enhanced Lévy flight bat algorithm, [ 42 ] and teaching–learning‐based artificial bee colony [ 43 ] algorithms have been used for PV parameter extraction. The results of the parameter extraction problem solved with these meta‐heuristic algorithms are presented under two categories: the simulation and the sensitivity analysis.…”
Section: Introductionmentioning
confidence: 99%
“…However, each parameter's values are affected by the operating temperature and irradiance, thus those values must also be noted. Xu and Qiu [9] have conducted research on the effective estimation of the unidentified model parameters for both the single diode model and the double diode model of solar cells and PV modules [15] suggest a modified stochastic fractal search technique. According to Calasan et al [16], the current-voltage characteristics of the double diode and triple diode models of solar cells exhibit significant nonlinearity, rendering them devoid of any analytical solution.…”
Section: Introductionmentioning
confidence: 99%