Wavelet Mutation based JAYA Optimization Algorithm for Global Maximum Power Peak Searching for Partially Shaded Solar PV Panel Condition

摘要

In this manuscript, a novel `Wavelet Mutation based JAYA Optimization (WMJO), algorithm is developed for GMPPS (Global Maximum Power Peak Searching) for partially shaded solar PV (Photovoltaic) panel condition. In classical GMPPS algorithms like Particle Swarm Optimization, Differential Evolution, Genetic Algorithm, etc., the major issues are, longer searching time during dynamic change condition, and computational complexity. The mitigation of these issues is the key contribution of this developed WMJO algorithm. In WMJO algorithm, the searching particles are pushed towards the global maximum peak by JAYA optimization algorithm, and the concept of Wavelet Mutation (WM) is used to explore the solution space. Therefore, the hybridized form of JAYA optimization algorithm and WM, WMJO algorithm searches GMPP (Global Maximum Power Peak) very accurately, which enhances the GMPPS efficiency in a steady-state condition. Moreover, in every iteration for dynamic condition detection, here an envelope of power is created, where the range is 5% below the lowest value of worst power and 5% above the highest value of the maximum achieved power in that iteration. In dynamic solar irradiance change condition, the envelope of power detects the situation and accordingly explores the searching particles to quickly capture the GMPP zone. Both these tactics increase the tracking efficiency in steady-state condition as well as decreases the GMPPS duration in dynamic irradiance change condition. The performance of WMJO algorithm is evaluated on different types of characteristics of solar irradiance pattern, through MATLAB simulation.