Analysis of AI Techniques for Maximum Power Point Tracking Under Partial Shading Conditions

With the increasing injection of renewables into the grid, PV has gained extensive attention. Multiple MPPT techniques have been and are being currently developed for an efficient tracking of the Maximum Power Point (MPP). The conventional tracking techniques do not work satisfactorily under the partial shading conditions and this has led to the use of artificial intelligence (AI) based techniques. These techniques guarantee a better response and a reduced deviation than the former. In this paper a comparative analysis is performed on Fuzzy Logic, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) based control techniques. AI based controllers can track the MPP with a desirable amount of accuracy under randomly varying irradiance conditions. The performance of compared techniques has been verified with the help of simulations for multiple test cases, the simulated results confirm that ANFIS based controller outperforms the FLC and ANN based controllers.