Parameter extraction of solar cell models using improved shuffled complex evolution algorithm

Gao, Xiankun; Cui, Yan; Hu, Jianjun; Xu, Guangming; Wang, Zhenfeng; Qu, Jianhua

doi:10.1016/j.enconman.2017.12.033

Cited by 250 publications

(133 citation statements)

References 68 publications

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“…France) silicon solar cell (under 1000 W/m 2 at 33°C) are used as the benchmark data [7]. This benchmark set has been widely used to evaluate the performance of different optimization algorithms [27,32,34,[37][38][39]. The lower and upper values of the solar cell parameters of both models are given in Table 1.…”

Section: Results On the Solar Cell Parameter Identificationmentioning

confidence: 99%

“…Regarding the popular metaheuristic algorithms, simulated annealing algorithm [12], genetic algorithm [13,14], particle swarm optimization algorithm [15,16], differential evolution algorithm [17][18][19][20], pattern search [21], artificial bee colony algorithm [22] are widely used for the SCPIP. In addition to these well-known heuristic algorithms, there exist several papers in the literature which consider more recent approaches, such as bacterial foraging algorithm [23,24], teaching-learning-based optimization algorithm [25][26][27], biogeography-based optimization algorithm [28], chaos optimization algorithm [29], artificial fish swarm algorithm [30], bird mating optimizer approach [31], artificial immune system [32], evolutionary algorithm [1], cat swarm optimization algorithm [33], moth-flame optimization algorithm [5], JAYA optimization algorithm [34,35], chaotic whale optimization algorithm [36], imperialist competitive algorithm [37], bee pollinator flower pollination algorithm [38], shuffled complex evolution algorithm [39], memetic algorithm [40], interior search algorithm [41], collaborative swarm intelligence approach [42], and cuckoo search algorithm [43]. On the other hand, it has been proven by No-Free-Lunch theorem [44] that none of these algorithms is able to solve all type of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Küçükoğlu

2019

International Journal of Photoenergy

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Solar cell parameter identification problem (SCPIP) is one of the most studied optimization problems in the field of renewable energy since accurate estimation of model parameters plays an important role to increase their efficiency. The SCPIP is aimed at optimizing the performance of solar cells by estimating the best parameter values of the solar cells that produce an accurate approximation between the current vs. voltage (I−V) measurements. To solve the SCPIP efficiently, this paper introduces an adaptive variant of the electromagnetic field optimization (EFO) algorithm, named adaptive EFO (AEFO). The EFO simulates the attraction-repulsion mechanism between particles of electromagnets having different polarities. The main idea behind the EFO is to guide electromagnetic particles towards global optimum by the attraction-repulsion forces and the golden ratio. Distinct from the EFO, the AEFO searches the solution space with an adaptive search procedure. In the adaptive search strategy, the selection probability of a better solution is increased adaptively whereas the selection probability of worse solutions is reduced throughout the search progress. By employing the adaptive strategy, the AEFO is able to maintain the balance between exploration and exploitation more efficiently. Further, new boundary control and randomization procedures for the candidate electromagnets are presented. To identify the performance of the proposed algorithm, two different benchmark problems are taken into account in the computational studies. First, the AEFO is performed on global optimization benchmark functions and compared to the EFO. The efficiency of the AEFO is identified by statistical significance tests. Then, the AEFO is implemented on a well-known SCPIP benchmark problem set formed as a result of real-life physical experiments based on single- and double-diode models. To validate the performance of the AEFO on the SCPIP, extensive experiments are carried out, where the AEFO is tested against the original EFO, AEFO variants, and novel metaheuristic algorithms. Results of the computational studies reveal that the AEFO exhibits superior performance and outperforms other competitor algorithms.

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Section: Results On the Solar Cell Parameter Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Küçükoğlu

2019

International Journal of Photoenergy

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“…where they fixed the ideal factors of the first and the second diode: n 1 = 1 and n 2 = 2. Otherwise, the system would have had nine parameters and the solution would have been more expensive for the fitness function to converge on the global minima, whilst avoiding being trapped in local minima [23].…”

Section: Main Idea Of the Discrete Symbiotic Organisms Search (Dsos)mentioning

confidence: 99%

“…Among the methods based on the gradient descent approach, we cite the Newton-Raphson method (NRM) [1][2][3][4]8,11,[23][24][25][26][27][28][29]. This method was formulated around an iterative calculation for solving a multivariable system of nonlinear equations (NLNRM).…”

Section: Main Idea Of the Discrete Symbiotic Organisms Search (Dsos)mentioning

confidence: 99%

A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search

2019

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Exact models are a necessary prerequisite for optimal hardware configuration and the design of high-performance controllers. The photovoltaic system is considered a dynamic nonlinear multimodal system, where an optimization method must be used to resolve non-linearity and to identify the parameters describing the models of such systems. This has incited several researchers to work on and to develop several optimization methods. Recently, a number of methods have been proposed, including deterministic approaches, as well as probabilistic and stochastic numerical approaches, that aimed at obtaining a more accurate model for the PV cell and module array. This paper demonstrates the application of a performance optimization method based on discrete symbiotic organism search (DSOS), that mimics the behaviors of an organism in an ecosystem to survive. The high performance of such a method is attributable to the simplicity of the algorithm used; this algorithm is different from other heuristic algorithms, in that the GA needs two tuning parameters, i.e., the cross over and mutation rate, while the harmony search needs three rules to adjust and improvise new harmony, being memory consideration, pitch adjustment, and random choosing. Meanwhile, in the ABC algorithm, three phases are introduced to find the best food source, that is, the employed bee, the onlooker bee, and the scout bee phases, while the DSOS algorithm did not need any tuning parameters, wherein the proposed algorithm was used in both a single diode and double diode model across three test cases in the study. Compared to other previously published works, the level of performance of the algorithm is high in both implementation and accuracy; the DSOS algorithm is more capable of reaching the best set of solutions. The Mann-Whitney-Wilcoxon test to evaluate the discrete solutions of the algorithm for multiple runs with a 5% degree of confidence was evaluated and performed with a good level of accuracy.

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“…Because of these advantages, different meta-heuristic methods have been applied to solve PV parameter estimation problems. Such as particle swarm optimization (PSO) [6], simulated annealing algorithm (SA) [7], genetic algorithm (GA) [8], pattern search (PS) [9], biogeography based optimization (BBO) [10], Artificial bee colony (ABC) [11], chaotic asexual reproduction (CAR) [12], adaptive differential evolution (ADE) [13], symbiotic organic search (SOS) [14], improved shuffled complex evolution (ISCE) [15], hybrid firefly algorithm and patter search (HFAPS) [16], multi learning backtracking search (MLBTS) [17], firefly algorithm (FA) [18], ant lion optimization (ALO) [19,28], particle swarm optimization/ adaptive mutation strategy (PSOAMS) [20], improved cuckoo search algorithm (ImCSA) [21], Lambert W function [22], improved teaching learning based optimization (ITLBO) [23], adaptive differential evolution [24], hybridizing cuckoo search / biogiography based optimization (BHCS) [25] and three point based approach (TPBA) [26], exploiting intrinsic properties [27].…”

Section: Introductionmentioning

confidence: 99%

Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization

Messaoud

2020

SN Appl. Sci.

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This article addresses the problem of estimating the uncertain parameters of solar photovoltaic models using Ant Lion Optimization. The parameter extraction phase consists of three stages, the first stage is devoted to estimating the parameters without considering the uncertainties, the second stage deals with estimating the uncertainties of each parameter, with the aim of reducing of search interval for parameters to extract. Finally, the parameters are extracted instantaneously taking into account the results of the first two steps. For validation of proposed theoretical developments, the algorithm is applied to the academic example of the real data of silicon solar cell RTC France. The results obtained are compared with well-established algorithms. These comparisons have shown that the proposed method presents largely effective performances compared to existing methods in the literature.

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Parameter extraction of solar cell models using improved shuffled complex evolution algorithm

Cited by 250 publications

References 68 publications

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

Adaptive Electromagnetic Field Optimization Algorithm for the Solar Cell Parameter Identification Problem

A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search

Extraction of uncertain parameters of double-diode model of a photovoltaic panel using Ant Lion Optimization

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