基于改进多种群粒子群算法的光伏组件参数辨识

针对光伏组件参数辨识问题,本文首先通过调整光伏单二极管超越方程重构出低计算复杂度的目标函数,又预估计模型参数对搜索空间进行优化,再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。

Photovoltaic module parameters identification based on an improved multi-group particle swarm optimization algorithm

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.