A novel musical chairs algorithm applied for MPPT of PV systems

Abstract

Due to the multiple peaks generated in the power to voltage characteristics of partially shaded photovoltaic (PV) arrays there is an urgent need for an effective optimization algorithm to capture its global peak instead of the local peaks. The required optimization algorithm should converge very fast and accurately capture the global peak. Many metaheuristic optimization algorithms have been introduced to tackle this problem and balance exploration and exploitation performances. These algorithms use a constant number of searching agents (swarm size) through all iterations. The maximum power point tracker (MPPT) of the PV system requires high numbers of searching agents in the initial steps of optimization to enhance explorations, whereas the final stage of optimization requires lower numbers of searching agents to enhance exploitations, which are conditions that are currently unavailable in optimization algorithms. This was the research gap that was the main motive of creating the new algorithm introduced in this paper, where a high number of searching agents is used at the beginning of the optimization steps to enhance exploration and reduce the convergence failure. The number of searching agents should be reduced gradually to have a lower number of search agents at the end of searching steps to enhance exploitation. This need is inspired by the well-known musical chairs game in which the players and chairs start with high numbers and are reduced one by one in each round which enhances the exploration at the start of the search and exploitation at the end of the search steps. For this reason, a novel optimization algorithm called the musical chairs algorithm (MCA) is introduced in this paper. Using the MCA for MPPT of PV systems considerably provided lower convergence times and failure rates than other optimization algorithms. The convergence time and failure rate are the crucial factors in assessing the MPPT because they should be minimized as much as possible to improve the PV system efficiency and assure its stability especially in the high dynamic change of shading conditions. The convergence time was reduced to 20%–50% of those obtained using five benchmark optimization algorithms. Moreover, the oscillations at steady state is reduced to 20%–30% of the values associated the benchmark optimization algorithms. These results prove the superiority of the newly proposed MCA in the MPPTs of the PV system.