基于改进灰狼优化算法的光伏MPPT方法

Under a partial shading condition, the P-U curve of the photovoltaic array output will have multiple peaks, which requires group intelligent algorithms with global optimization ability to conduct maximum power point tracking ( MPPT) . The conventional algorithm has many problems, such as slow convergence speed, large amplitude of oscillation and easy to fall into local optimum. A control method based on improved grey wolf optimization algorithm is proposed in this paper. The algorithm adopts the strategy of interval contraction to reduce the search interval and improve the convergence speed and solution accuracy. Meanwhile, the reverse optimization strategy is used by searching the reverse solution of the current optimal solution to increase the diversity of the search process and help the algorithm jump out of the local optimal. Statistics results of simulation show that the improved algorithm has higher tracking success rate, accuracy and less tracking time than the basic algorithm.