基于混沌搜索的改进狮群算法及其在光伏电池参数辨识中的应用

目前已有多种智能算法应用到光伏电池模型的参数辨识中,然而大都存在易陷入局部最优、收敛速度慢等问题,基于改进狮群算法,提出了一种有效的光伏电池参数辨识方法。首先,通过引入混沌初始化、自适应参数和混沌搜索,弥补了狮群算法收敛速度慢、寻优精度不高等不足;将改进狮群算法应用到光伏电池的单二极管模型和双二极管模型的参数辨识中,与5种优化算法的结果进行对比,证明了该算法在光伏电池参数辨识中的有效性和优越性;最后,通过在不同辐照度和不同天气类型下进行辨识,探究了外部环境变化对模型参数的影响,进一步验证了该算法的有效性和实用性。

Lion Swarm Optimization Based on Chaotic Search Strategy and Application in Parameters Identification of Photovoltaic Cell Models

Up to now, many intelligent algorithms have been used in parameter identification of photovoltaic cell model, but most of them are prone to local optimum and the convergence speed is slow. Lion swarm optimization is a novel intelligent algorithm proposed in recent years, and it also has the problems mentioned above. An improved lion swarm algorithm based on chaotic search strategy (CLSO) was proposed. By introducing chaotic sequence, adaptive parameter and tent chaotic search strategy, the deficiency of the lion swarm optimization was remedied. Firstly,the algorithm was applied to the parameter identification of single-diode model and double-diode model of photovoltaic cells, compared with the results of other five algorithms, which proved the effectiveness and superiority of this algorithm in the parameter identification of photovoltaic cells. Besides, experiments were carried out under different irradiance and different weather types, exploring the influence with changing external environment on model parameters, which further verify the effectiveness and practicability of the algorithm.