基于权重指数递减粒子群算法在光伏MPPT中的应用

针对光伏阵列局部遮阴情况下输出电压-功率曲线呈现多峰特性,传统粒子群算法进行最大功率跟踪时会陷入局部最优的问题,提出了权重指数递减粒子群算法。该算法通过改变粒子搜索方式,在每次迭代结束前对搜寻到的最优粒子执行精英突变,对反方向空间进行搜索；并添加惯性权重调节参数,其惯性权重随迭代次数的增加以指数形式递减,使算法前期跳出局部最优点的能力提高以及后期搜索更加准确。仿真结果表明,该算法在遮阴或者光照突变情况下,均能准确的追踪到最大功率点,能有效避免陷入局部最优点,收敛速度较快,能够在复杂情况下实现最大功率追踪。

Application of weighted index declining particle swarm optimization algorithm in photovoltaic MPPT

The output voltage-power curve exhibits multi-peak characteristics for the local shading of the PV array. The traditional particle swarm optimization algorithm will fall into the local optimal problem when the maximum power tracking is performed. In this paper, the weight index decreasing particle swarm optimization (PSO) algorithm is proposed. By changing the particle search method, the elite mutation is performed on the searched optimal particle before the end of each iteration, and the reverse direction space is searched; and the inertia weight adjustment parameter is added, and the inertia weight is exponentially increased with the number of iterations. Decrement, so that the ability of the algorithm to jump out of the local best advantage in the early stage and the later search is more accurate. The simulation results show that the algorithm can accurately track the maximum power point under the condition of shading or sudden change of light, which can effectively avoid the local best advantage, and the convergence speed is fast, which can achieve maximum power tracking under complex conditions.