A state-of-the-art review on shading mitigation techniques in solar photovoltaics via meta-heuristic approach

To enhance the production of solar photovoltaic (SPV)-based cleaner energy, the maximum power point (MPP) tracking (MPPT) schemes are utilized. To ensure a reliable and effective MPP extraction from SPV systems, the exploitation and implementation of different MPPT schemes are of great significance. This article intends to present a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading (PS) conditions based on a meta-heuristic approach and artificial neural network (ANN). In recent years, modern optimization-based global MPP (GMPP) extraction schemes are gaining much attention from researchers. In this review article, thirteen modern optimizations and ANN-based GMPP tracking techniques are vividly described with their flowchart and detailed mathematical modeling. This work assesses all the schemes according to parameters like tracking efficacy, tracking time, application, sensed parameters, converter utilized, steady-state oscillations, experimental setup, and key notes. Based on the rigorous review, a novel GMPP extraction scheme based on a recently introduced meta-heuristic approach named artificial gorilla troops optimizer is proposed. This review work serves as a source of comprehensive information about applying these MPPT techniques to extract GMPP from the SPV system under PS conditions; furthermore, it can be considered a one-stop handbook for further study in this field.

Automated summary

This article provides a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading conditions, utilizing meta-heuristic approaches and artificial neural networks. It highlights the importance of different MPPT schemes in ensuring reliable and effective MPP extraction for solar photovoltaic systems.