Parameter extraction of solar cell models using chaotic asexual reproduction optimization

Yuan, Xiaofang; He, Yi; Liu, Liangjiang

doi:10.1007/s00521-014-1795-6

Cited by 58 publications

(36 citation statements)

References 42 publications

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“…Then, by replacing Equation (4) in Equation (1), the PV cell's net current flow is now given as: As shown in Figure 1, the ideal cell's output voltage correspond to the voltage across the diode D (that is, V cell = V D ). With that being said, and by replacing the term I D given by Equation (2) into Equation (1), the PV cell's current is then given as:…”

Section: Single-diode Modelmentioning

confidence: 99%

“…In recent years, several economic and environmental phenomenon, such as the non-stopping increase on the cost of fossil fuel along with its probable depletion in the near future, the dramatic increase on the air pollution, and the ever worrying climatic changes and global warming effect, have motivated an increasing trend on the use of renewable energy sources [1].…”

Section: Introductionmentioning

confidence: 99%

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A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

et al. 2017

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Solar energy is used worldwide to alleviate the daily increasing demands for electric power. Photovoltaic (PV) cells, which are used to convert solar energy into electricity, can be represented as equivalent circuit models, in which a series of electrical parameters must be identified in order to determine their operating characteristics under different test conditions. Intelligent approaches, like those based in population-based optimization algorithms like Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Simulated Annealing (SA), have been demonstrated to be powerful methods for the accurate identification of such parameters. Recently, chaos theory have been highlighted as a promising alternative to increase the performance of such approaches; as a result, several chaos-based optimization methods have been devised to solve many different and complex engineering problems. In this paper, the Chaotic Gravitational Search Algorithm (CGSA) is proposed to solve the problem of accurate PV cell parameter estimation. To prove the feasibility of the proposed approach, a series of comparative experiments against other similar parameters extraction methods were performed. As shown by our experimental results, our proposed approach outperforms all other methods compared in this work, and proves to be an excellent alternative to tackle the challenging problem of solar cell parameters identification.

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Section: Single-diode Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

et al. 2017

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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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“…Owing to these features, chaos is widely applied to the global optimization, and chaos optimization algorithm (COA) is emerged [3]. It is known that searching for global optimum with chaotic sequence is often superior to that with random search [4][5][6]. In fact, the chaotic search is easy to escape from local optimum due to its ergodicity and pseudorandom, etc.…”

Section: Introductionmentioning

confidence: 99%

Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization

Yang

Liu

2016

Neural Comput & Applic

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A new hybrid chaos optimization algorithm (COA), namely, the accelerated particle swarm optimization combined with different chaotic maps (APSOC), is proposed for global optimization with continuous and discrete variables in this paper. Computational efficiency of APSOC and other three COAs (CPSO1-CPSO3) is compared for nonlinear benchmark functions. And the three influencing factors of chaotic maps on efficiency are considered, namely, the Lyapunov exponent (LE) which quantifies the search speed of chaotic sequence, the probability distribution function (PDF), and the dispersion degree of chaotic sequence which is defined as an index to measure the computational performance of evolutionary algorithm herein. To investigate the influence of CPSOs with different one-dimensional chaotic maps on efficiency of global optimization, three cases are examined, such as: different chaotic maps with close LE and different PDF; the same chaotic map with the same PDF and different LE; and the identical chaotic map with equal or close LE and different PDF. Optimization results demonstrate that the probability distribution, search speed, and dispersion degree of chaotic sequences affect remarkably the performance of CPSOs. Finally, statistic results and evolution curves of APSOC with Circle map are compared with those of other three COAs, and the optimal design of trusses with discrete variables are performed by APSOC. It is indicated that APSOC with Circle map is superior to other CPSOs and has greater exploration ability and faster convergence rate.

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Parameter extraction of solar cell models using chaotic asexual reproduction optimization

Cited by 58 publications

References 42 publications

A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

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

Computational efficiency of accelerated particle swarm optimization combined with different chaotic maps for global optimization

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