Nature-Inspired Optimization Algorithms in Solving Partial Shading Problems: A Systematic Review

Artificial intelligence based maximum power point tracking (MPPT) techniques play an essential role in improving the efficiency of photovoltaic power conversion systems. Over the past few years, researchers around the world have proposed various nature-inspired metaheuristic optimization algorithms in order to extract the highest possible power from photovoltaic (PV) systems under partial shading conditions. These approaches were developed to track for the maximum power point (MPP) efficiently with fast convergence speed and high accuracy. This paper provides a systematic review on these state-of-the-art computing mechanisms with their recent advancements, modifications and adaptations in tracking for the MPP of PV systems under partial shading conditions. The technical advantages, trade-offs, and challenges of these computation mechanisms are analysed and discussed. In-depth study found that nature-inspired swarm search mechanisms are highly suitable to be implemented as MPPT schemes in PV applications. Recent developments and improvements show enhancements in multiple different aspects, especially in the accuracy and the speed of the search algorithms. Several research gaps are identified and discussed to guide future research directions.