Performance and Optimization of MPPT Techniques for Modeling, and Control of Solar PV system

Abstract

Today the power sector requirement is increasing 
continuously and reserve of fossil fuel is limited so we have 
already moved toward renewable generation. Demand of
renewable sources of energy should be our prime focus to 
mitigate the power requirement. The solar power generation is 
of the best choice for power generate because it is freely 
available. Maximum power point tracking (MPPT) techniques 
is one of the most useful method to get maximum power at any 
instant of time. Classical MPPT techniques fail to provide an 
accurate output power thus; optimization of MPPT techniques 
play an important role in maximization of output power. 
Considering the dependency on renewable energy uses, this 
paper, presents various types of optimization to track MPPT 
techniques implemented on Photovoltaic (PV) system. These 
techniques applied for solar system is helpful in designing and 
improving efficiency of the PV system. Due to non linear 
characteristics of PV array a non-linear controller is most 
suitable for MPPT applications. The paper, first describe 
different types of characteristics of solar PV cell used for MPPT 
technique and followed by different optimization techniques 
incorporating fazzy, neural network Grey Wolf Optimization 
(GWO), Simplified Firefly Algorithm (SFA), Enhanced Grey 
Wolf Optimization (EGWO), Particle Swarm Optimization 
(PSO), etc have been discussed. Performance has been analyzed 
based on efficiency, tracking speed, converter used, application 
and implementation cost etc.