Reduced Simulative Performance Analysis of Variable Step Size ANN Based MPPT Techniques for Partially Shaded Solar PV Systems

The rise in energy demand in the present scenario can be balanced with the help of solar Photovoltaic (PV) systems. But, the nonlinearity in I-V and P-V characteristics makes it very difficult to extract the maximum power of the solar PV. Also, the classical Maximum Power Point Tracking (MPPT) techniques fail to track the global Maximum Power Point (MPP) from the multiple local MPPs under Partial Shading Conditions (PSCs). In this work, a Variable Step Size ANN-based MPPT technique is proposed and it is compared with the other MPPT techniques in terms of steady-state behavior, settling time of converter power, power point tracing speed, oscillations of MPP, and operating efficiency. The compared MPPT techniques are Adaptive Perturb & Observe (AP&O), Adaptive Feed Forward Neural Network Controller (AFFNNC), Artificial Neural Network-based P&O (ANN-based P&O), ANN-based Incremental Conductance (ANN-based IC), ANN-based Hill Climb (ANN-based HC), and Radial Basis Functional Controller based Fuzzy (RBFC based Fuzzy). The boost converter is interfaced in the middle of the PV system and load to step-up the PV supply voltage. The performance of selected neural networks MPPT techniques is studied by utilizing a MATLAB/Simulink window.