Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm
Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm
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使用改进的简化群优化算法提取太阳能电池模型的参数
DOI：
10.1016/j.solener.2017.01.064
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发表时间：
2017-03
期刊：
影响因子：
6.7
通讯作者：
Lijun Wu
中科院分区：
文献类型：
--
作者：
Peijie Lin;Shuying Cheng;Weichang Ye;Zhicong Chen;Lijun Wu
太阳能电池模型的参数对太阳能电池的仿真结果有很大的影响，可用于光伏系统中光伏组件的工作状态监测和潜在故障诊断。为了在有限的CPU运行时间内准确、高效地提取太阳能电池的最优参数，提出了一种改进的简化群优化（MSSO）算法，该算法以最小化计算值与实验值之间的最小二乘误差为目标，分别针对单二极管和双二极管模型进行求解。在多点单点登录中，采用了一种新的单变量更新机制和优胜劣汰策略来增强传统单点登录的能力。为了研究MSSO的性能，与其他众所周知的优化算法进行了比较研究，即，SSO，人工蜂群（ABC）和简化的鸟类交配优化器（SBMO），并显示了广泛的计算结果。统计数据表明，MSSO方法具有最好的性能，在这些方法的效率，鲁棒性和准确性。此外，由MSSO提取的参数的电流-电压特性与实验数据吻合良好。
The parameters of solar cells models have an effect on the simulation of solar cells and can be applied to monitor the working condition and diagnose potential faults for photovoltaic (PV) modules in a PV system. To accurately and efficiently extract the optimal parameters of solar cells in a limited CPU run time, a modified simplified swarm optimization (MSSO) algorithm is presented for the single diode and double diode models by minimizing the least square error between the calculated and experimental data. In MSSO, a new one-variable-update mechanism and survival-of-the-fittest policy are applied to enhance the ability of traditional SSO. To investigate the performance of MSSO, comparative studies with other well-known optimization algorithms, i.e., SSO, artificial bee colony (ABC) and simplified bird mating optimizer (SBMO), are presented, and extensive computational results are shown. The statistical data indicate that the MSSO method has the best performance among these methods in terms of efficiency, robustness and accuracy. Moreover, the current vs. voltage characteristics of the parameters extracted by MSSO coincide well with those of experimental data.
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DOI：
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发表时间：
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期刊：
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