Maximum power extraction from solar PV systems using intelligent based soft computing strategies: A critical review and comprehensive performance analysis

Abstract

This paper shows a comprehensive review on various maximum power point tracking (MPPT) techniques of the solar photovoltaic (PV) cell. It is well understood that power from a solar PV array is sometimes not sufficient, so it is required to extract the maximum power to meet the load demand. In this regard, different techniques were used for comparative analysis like perturb and observe (P & O), fuzzy logic control (FLC), incremental conductance (IC), ripple correction control (RCC), artificial neural network (ANN), particle swarm optimization (PSO), lyapunov control scheme (LCS), and fisher discrimination dictionary learning (FDDL). The performance of MPPT is also examined under the conditions like effect of shading, irradiance, etc. After reviewing the literature, it has been observed that maximum power at different sets of irradiations is extracted with ANN in comparison to other techniques. Subsequently, the least deviations about maximum power point are attained with IC while comparing with other techniques and FDDL has been found the best technique for attaining the minimum total harmonic distortion (THD). In addition to this, it is also detected that the least switching losses are attained with PSO in comparison to others. To this end, it has been concluded that each method has its significance for the extraction of maximum power from the source and dominance over other methods for smart energy systems. The researchers may find this critical review to be a valuable resource in choosing an appropriate soft computing method for the given parameters.