Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm

Guo, Lei; Meng, Zhuo; Sun, Yize; Wang, Libiao

doi:10.1016/j.enconman.2015.11.041

Cited by 259 publications

(96 citation statements)

References 31 publications

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“…The results of the CIABC are compared with similar optimization algorithms. These techniques are chaos particle swarm optimization (CPSO) [19], simulated annealing (SA) [37], cat swarm optimization (CSO) [2], simplified teaching-learning based optimization (STLBO) [23], generalize depositional teaching learning-based optimization (GOTLBO) [46], artificial bee swarm optimization [6], harmony search (IGHS) [1], artificial bee colony (ABC) [21] and modified artificial bee colony (MABC) [47].…”

Section: Resultsmentioning

confidence: 99%

“…These situations occur primarily because the technology is not completely developed. Another cause is the outdoors environment that directly affects the solar modules making necessary their frequent replacement [2]. In the same context, the efficiency of photovoltaic modules (or PV cells) depends on environmental factors such as temperature or radiation that cannot be controlled [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

et al. 2017

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The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun's radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

et al. 2017

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show abstract

“…A comparative study has also been presented with the other techniques like; genetic algorithm. It has been observed that the parameters optimized with CSO has high accuracy and good agreement with the experimental voltage-current data [3]. With the continuation of the optimization technique, the work has been carried out on single channel glazed photovoltaic thermal system.…”

Section: Introductionmentioning

confidence: 87%

Efficiency maximization and performance evaluation of hybrid dual channel semitransparent photovoltaic thermal module using fuzzyfied genetic algorithm

Singh

Agrawal

2016

Energy Conversion and Management

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“…It has been demonstrated that the applications of intelligent algorithms can bring about better performance or improved designs. There are several researches on applications of intelligent algorithms to parameters identification and control system design, such as [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Zheng

Liao

2016

Optik

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Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm

Cited by 259 publications

References 31 publications

A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

Efficiency maximization and performance evaluation of hybrid dual channel semitransparent photovoltaic thermal module using fuzzyfied genetic algorithm

Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

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