Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions

In solar PV (photovoltaic) system, tracking the module's MPP (maximum power point) is challenging due to varying climatic conditions. Moreover, the tracking algorithm becomes more complicated under the condition of partial shading due to the presence of multiple peaks in the power voltage characteristics. This paper presents a NN (neural network) based modified IC (incremental conductance) algorithm for MPPT (maximum power point tracking) in PV system. The PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink simscape tool box. The simulated system was evaluated under uniform and non-uniform irradiation conditions and the results are presented. For comparison, P and O (perturb and observe) and Fuzzy based Modified Hill Climbing algorithms were used for MPP tracking, and the results show that the proposed approach is effective in tracking the MPP under partial shading conditions. To validate the simulated system hardware implementation of the proposed algorithm was carried out using FPGA (Field Programmable Gate Array). - Highlights: • SIMSCAPE based photovoltaic system modeling with partial shading effect. • The simple, ease of implementation and low cost MPPT algorithms are proposed. • Comparison study of two more MPPTs with proposed MPPT. • Without additional circuit or complex calculation, to reach the goal of MPPT. • ANN is used to provide reference voltage