Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm
Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm
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DOI：
10.1016/j.enconman.2015.11.041
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发表时间：
2016-01-15
影响因子：
10.4
通讯作者：
Wang, Libiao
中科院分区：
文献类型：
--
作者：
Guo, Lei;Meng, Zhuo;Wang, Libiao
太阳能电池模型广泛应用于光伏系统的各种研究中。已经开发了不同的方法来确定模型参数。提出了一种基于猫群优化(CSO)算法的优化技术来估计单、双二极管模型的未知参数。为了考察所提出的方法的有效性，给出了与其他技术的比较研究。并对辨识后的参数质量进行了评价。结果表明，该方法性能优良，参数估计精度高，计算的I-V曲线与实验数据吻合较好。此外，还研究了性能对CSO控制参数的敏感性。结果表明，所提出的CSO算法是解决太阳电池模型参数辨识优化问题的有效工具。(C)2015爱思唯尔有限公司。保留所有权利。
Solar cell model is used in various studies of photovoltaic system. Different methods have been developed to determine model parameters. In this paper, an optimization technique based on cat swarm optimization (CSO) algorithm is proposed to estimate the unknown parameters of single and double diode models. To investigate the effectiveness of proposed approach, comparative studies with other techniques are presented. The evaluation for the quality of identified parameters is also given. Results demonstrate the high performance of developed approach, high accuracy of estimated parameters, and calculated I-V curve is in good agreement with experimental I-V data. In addition, the sensitivity of performance to control parameter of CSO is also investigated. Results show the proposed CSO algorithm can be an effective tool to solve the optimization problem of parameter identification of solar cell models. (C) 2015 Elsevier Ltd. All rights reserved.