基于差分进化人工蜂群算法的光伏最大功率跟踪策略研究

Research on maximum power point tracking strategy based on differential evolution artificial bee colony algorithm of photovoltaic system

带有旁路二极管的光伏阵列在局部阴影时其P-U特性曲线会出现多个极值点,此时常规MPPT方法在多峰值寻优时可能会失效。对光伏阵列输出特性功率极值点的个数进行了研究,在此基础上将基于差分进化的人工蜂群算法应用于最大功率点跟踪。首先对蜜蜂的初始位置进行预定义初始化,避免遗漏极值点。将差分进化算法中的变异策略与人工蜂群算法相结合,实现时变条件下全局最大功率点跟踪控制。并且在上述算法中加入迭代终止策略,从而有效避免系统稳态时的功率振荡现象。在Matlab中搭建S-Function仿真模型,验证了该算法的有效性。

The P-U characteristics of the PV array with bypass diodes have multi local maximum power points under partially shadowed conditions, but Conventional Maximum Power Point Tracking（CMPPT） methods may fail to identify global maximum power points. The number of the extreme points in the output characteristics of PV array is studied, and the artificial bee colony algorithm based on differential evolution is applied to the MPPT. First, the initial positions of the bees are pre-defined to avoid missing the extreme points. Combining the mutation strategy in the differential evolution algorithm with the artificial bee colony algorithm to realize the global maximum power point tracking control under time-varying conditions, and efficient iteration stop strategy is proposed which could reduce the power oscillation when trending to steady state. The S-Function model is built in Matlab to verify the effectiveness of the algorithm.