For Increase the Performance of Solar PV by Using ANFIS with GWO Based MPPT Controller
Abstract
In this real world solar energy is the free and most beneficial renewable source directly from the sunlight. However, preservation of solar energy and utilize in proper manner is the important factor for better economic growth in this real-world. There are several material used to utilize the solar energy to electricity. In this research study, we implement the two effective techniques for the maximum power with reduced power oscillations during different condition. In this study, we basically construct the solar PV system as on grid setup, to track the maximum power level by using Maximum Power Point Tracker (MPPT). And also to improve the efficiency and faster to track the MPP level by using Hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) and Grey Wolf Optimization (GWO) algorithm, which also perform to optimize the membership function. Anti-Islanding Grid Protection also implemented to protect the system, when blackout occur in the grid system. The implemented algorithm is train and test process by using the Photovoltaic current and voltage data. The experimental performance result is obtained by using MATLAB tool. The proposed method provide better performance under operating at regular test condition with unvarying solar irradiation and partial shading. And also proposed method performance is compares by using different parametric metrics with different methods as ANFIS- Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee colony. In this comparison, proposed model provide the better efficiency under different condition.