A Comprehensive Evaluation of Up-to-Date Optimization Algorithms on MPPT Application for Photovoltaic Systems

Maximum Power Point Tracking (MPPT) plays a significant role in obtaining maximum power at PV system outputs. Recent research has focused on minimizing the adverse effects of partial shading and dynamic environmental conditions on MPPT. Metaheuristic optimization algorithms have attracted attention with their success in these issues. The growing body of literature lacks a study that comprehensively evaluates current metaheuristic algorithms. In this study, the performances of 20 metaheuristic algorithms under five different shading conditions, nonuniform temperature distribution, and variable irradiance conditions have been investigated. The successes of the algorithms in the convergence of the global maximum value and their convergence rates have been calculated through various statistical metrics with different aspects. This study provides a novel approach to objectively evaluating the performances of the algorithms by using the three-dimensional Pareto Front method. As the result of this multicriteria evaluation, RKO, MPA and CGO algorithms are able to provide non-dominated results. These three algorithms are further tested using case analysis designed for dynamic operating conditions, and the RKO algorithm exhibited the most favorable results. Additionally, the RKO algorithm exhibits remarkable performance by reaching the LMPP/GMPP point within an average time of 3.2 milliseconds in all cases. Moreover, it demonstrates an average efficiency value exceeding 0.999.