Maximum power point tracking of photovoltaic array based on improved Particle Swarm Optimization Algorithm

Abstract

With the vigorous development of the photovoltaic industry, how to improve the efficiency of photovoltaic power generation has become an important issue, among which partial shadow occlusion is an important reason affecting the efficiency. The efficiency of photovoltaic power generation can be effectively improved by adopting the maximum power point tracking method (MPPT), but the traditional MPPT method is not ideal in the partial shadow occlusion of the photovoltaic array. To solve this problem, this paper proposes an improved particle swarm optimization method to effectively improve the tracking efficiency of MPPT when multiple peaks appear in the photovoltaic arrays power curve (P-V) under the partial shadow. The proposed method improves the learning factor of the traditional particle swarm optimization algorithm and designs the initial position of the particles according to the characteristics of the photovoltaic array. By adding the particle elimination mechanism, the number of particles changes dynamically, and the tracking speed of the algorithm for the maximum power of the photovoltaic array is improved. Through the result of the simulation, it is not difficult to get the conclusion that the improved particle swarm optimization algorithm can effectively improve the performance of the photovoltaic system under partial shadows.

MPPT Control Research of Improved Gray Wolf Algorithm According to Levy Flight and Greedy Strategy

“…Compared with the traditional MPPT control algorithm, it can effectively track the maximum power globally, but there are still some problems such as the convergence speed is slow, and problems with low precision and large amplitude. In addition, the Artificial Fish Swarm Algorithm (AFSA) [10], Ant Colony Algorithm (ACA) [11] and Cuckoo Search (CS) [12] also shows effectiveness, but there are many problems such as large adjustment parameters and large oscillation amplitude. Literature [13] proposed the Grey Wolf Optimizer (GWO) algorithm with high solution accuracy and few adjustment parameters, which is applied to photovoltaic array multimodal MPPT.…”