Enhanced MPPT for PV systems using a two-stage hybrid fuzzy logic and P&O algorithm

Abstract

Photovoltaic (PV) systems have converted solar irradiance into electrical energy through PV cells that exhibit nonlinear voltage-current (V-I) characteristics. A key feature of these characteristics has been the maximum power point (MPP), where the product of voltage (V) and current (I) has reached its maximum, enabling optimal power extraction. The efficient operation of a PV system has required rapid and accurate Maximum Power Point Tracking (MPPT), especially in dynamically changing environmental conditions. This paper has presented a hybrid MPPT approach that has combined a Fuzzy Logic Controller (FLC) with the conventional Perturb and Observe (P&O) algorithm. The proposed two-stage control scheme has continuously identified and adjusted the MPP in response to variations in irradiance and temperature, including partial shading (PS) scenarios. In the first stage, the FLC has provided an intelligent initial estimate of the MPP region to improve the convergence speed of the P&O algorithm. In the second stage, another FLC has dynamically adjusted the step size of the P&O algorithm to improve response and reduce oscillations. The hybrid FLC-P&O algorithm has been validated through simulations under various conditions, including steady irradiance, sudden changes in sunlight, and partial shading of varying severity. The results have demonstrated that the proposed controller has achieved high tracking efficiency and has effectively overcome the limitations of the conventional P&O algorithm, particularly under non-uniform and rapidly changing environmental conditions.