Artificial Intelligent Parameter based PSO for Maximum Power Point Tracking of PV Systems under PSC

In this paper, an artificial intelligent based inertia weight is incorporated in traditional particle swarm optimization (FLC_PSO) technique is applied for tracking the maximum power operating point of photovoltaic systems efficiently. Even though PSO based MPPT technique is much effective in tracking exact local MPPs show poor success rates in tracking global MPPs by the great dependence of randomly chosen weight. With this sensation, IPSO is developed which provides linear variation in the inertia weight and it does not give exact search space in dealing shaded conditions. Thus Fuzzy system is developed for the dynamic inertia weight in PSO algorithm ensures extended nonlinear search space to speed up the convergence of global MPP under partial shaded conditions. Here FLC_PSO based MPPT is proposed to control the duty cycle of the DC-DC converter guarantees extended nonlinear search space for tracking the maximum power point with faster dynamic response. Thus the proposed methodology is verified using Matlab/SIMULINK software tool.