MPPT of PV system under partial shading condition based on adaptive inertia weight particle swarm optimization algorithm

TLDR: Simulation results for constant partial shading and rapid changing partial shading show that the proposed AIWPSO algorithm can avoid premature convergence effectively and has good global searching capability.

Abstract: This paper adopts an adaptive inertial weight particle swarm optimization (AIWPSO) algorithm to improve the maximum power point tracking (MPPT) capability for photovoltaic (PV) system under partial shading condition. Partial shading is a common phenomenon in PV generation system, it causes imbalance and decreases for output power of PV array. Under partial shading condition, output characteristics of PV system will change and the P-V characteristic curve contains more than one peak, which makes the conventional algorithm for MPPT is difficult to track the practical MPP. Particle swarm optimization (PSO) algorithm is often used in MPPT under partial shading condition, but PSO algorithm has the disadvantages of low convergence speed and search accuracy. In this paper, AIWPSO algorithm is proposed to solve these problems. In AIWPSO algorithm, a nonlinear dynamic inertia weight factor is introduced into the PSO evolution to improve global searching ability of PSO algorithm. Simulation results for constant partial shading and rapid changing partial shading show that the proposed algorithm can avoid premature convergence effectively and has good global searching capability.