文章摘要
引用本文:吴 越,陈志聪,吴丽君,林培杰,程树英,陆培民.改进多种群粒子群算法辨识光伏组件参数[J].福州大学学报(自然科学版),2017,45(1):108~114
改进多种群粒子群算法辨识光伏组件参数
Photovoltaic module parameters identification using an improved multi-group particle swarm optimization algorithm
  
DOI:10.7631/issn.1000-2243.2017.01.0108
中文关键词: 光伏组件  参数辨识  N-MPSO算法
英文关键词: PV module  parameter identification  N-MPSO algorithm
基金项目:
作者单位
吴 越 福州大学微纳器件与太阳能电池研究所福建 福州 350116 
陈志聪 福州大学微纳器件与太阳能电池研究所福建 福州 350116 
吴丽君 福州大学微纳器件与太阳能电池研究所福建 福州 350116 
林培杰 福州大学微纳器件与太阳能电池研究所福建 福州 350116 
程树英 福州大学微纳器件与太阳能电池研究所福建 福州 350116 
陆培民 福州大学微纳器件与太阳能电池研究所福建 福州 350116 
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中文摘要:
      针对光伏组件参数辨识问题,通过调整光伏单二极管超越方程重构低计算复杂度的目标函数,以预估计模型参数对搜索空间进行优化. 然后,结合多种群粒子群算法与单纯形算法的优点,构造出N-MPSO混合新算法用于光伏组件模型参数的精确稳定辨识. 最后,利用多种实际光伏组件测量数据对所提方法进行检验. 结果表明:N-MPSO算法相较于传统算法能够更加准确、快速,且能稳定地辨识出任意环境条件下光伏组件的模型参数,对于光伏组件及光伏电站的设计、测试与诊断具有实际意义.
英文摘要:
      Addressing the issue of photovoltaic module parameters identification,a new hybrid algorithm based on multi-group particle swarm optimization and simplex method is proposed. Firstly,the transcendental equation of the single diode photovoltaic model is modified so as to greatly reduce the computation complexity. Secondly,the search space for the parameters is optimized by pre-estimating the parameters initial value. And then,combining the advantage of multi-group particle swarm optimization and simplex method,a hybrid N-MPSO algorithm is constructed to quickly obtain the stable and accurate parameters. Finally,the algorithm is validated by several groups of I-V data measured from some typical photovoltaic modules. The results show that the proposed N-MPSO algorithm can reach a higher accuracy and lower time complexity compared with some other conventional methods,which is significant to the design,testing and diagnosis of photovoltaic modules and power stations.
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