A grey wolf optimization algorithm based mppt with integrated dc dc converter fed multilevel inverter for a standalone pv system

Abstract

Renewable energy sources are clean, green with no carbon dioxide emission. Nowadays due to the rising serious issues related with global warming and the life threatening carbon emissions around the globe, the awareness about the problems are increasing. This had created a new demand for renewable resources. The major renewable techniques consist of biomass, hydro power, solar power, and wind power and ocean energy. Among these solar power is the most significant source. As the solar insolation is varying, multiple operating points are obtained in the PV system characteristics. Various optimization and intelligent techniques such as Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search Algorithm (CS), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN) and Hybrid techniques used for tracking maximum power point are detailed in the literature. The operation and tracking in the conventional PandO method is satisfactory when the insolation changes slowly. But the convergence time is more and also the output is oscillatory for rapid insolation changes. The requirement of high precision and fast-speed digital processor for implementation increases the overall cost of the system. However the fuzzy logic controller leads to inaccuracy by the inappropriate and complex tuning rules and merging of membership functions. The accurate and quick tracking of MPPT under static and dynamic conditions with high tracking efficiency and reduced power fluctuation in real time makes Grey Wolf Optimization (GWO) algorithm to be notified as the better option newline