Solar photovoltaic (PV) systems are attracting a huge focus in the current energy scenario. Various maximum power point tracking (MPPT) methods are used in solar PV systems in order to achieve maximum power. In this article, a clear analysis of conventional MPPT techniques such as variable step size perturb and observe (VSS-P&O), modified incremental conductance (MIC), fractional open circuit voltage (FOCV) has been carried out. In addition, the soft computing MPPT techniques such as fixed step size radial basis functional algorithm (FSS-RBFA), variable step size radial basis functional algorithm (VSS-RBFA), adaptive fuzzy logic controller (AFLC), particle swarm optimization (PSO), and cuckoo search (CS) MPPT techniques are presented along with their comparative analysis. The comparative analysis is done under static and dynamic irradiation conditions by considering algorithm complexity, tracking speed, oscillations at MPP, and sensing parameters. The single-diode model PV panel and double-diode model PV panel are also compared in terms of fill factor (FF) and maximum power extraction. Clear insight is presented supporting the suitability of MPPT techniques for different types of converter configurations.

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 solar PV. Also, the classical Maximum Power Point Tracking Techniques (MPPT) fail to track the global Maximum Power Point (MPP) from the multiple local MPPs under Partial Shading Conditions (PSCs). In this work, Variable Step Size ANN-based MPPT Techniques are studied and are compared in terms of steady-state behavior, settling time of converter power, power point tracing speed, oscillations of MPP, and operating efficiency. The discussed 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 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.