Integration of Renewable Energy Sources With Intelligent Neuro Fuzzy Control for Microgrid System

Abstract

As microgrids (MGs) deploy stability and continuous power flow in utility grids it acts as a remedial measure for power grid failure and high load demand. A hybrid Renewable Energy Source (RES) fed microgrid with an Adaptive Neuro fuzzy Inference System (ANFIS) controller is suggested. The photovoltaic (PV) system and Doubly Fed Induction Generator fed Wind Turbines (DFIG-WT) are selected as a typical RES. Among various converters in use, a novel high gain Landsman converter is utilized to attain consistent DC-link voltage and to boost the photovoltaic output for the generation of high efficiency outputs. ANFIS method, which is a significant alternative designed with the combination of two computing approaches of fuzzy logic and Artificial Neural Network (ANN) is used for the converter control. To increase the convergence rate and reliability prediction rate, the constraints of ANFIS are improved using Crow Search Algorithm (CSA). Subsequently, the wind system with Pulse Width Modulation (PWM) rectifier regulated by Proportional Integral (PI) controller is connected to the microgrid setup. A battery energy storage system (BESS) is adopted for the storage of energy during surplus conditions while providing energy to DC link at times of power shortage from PV and wind sources. Assessments on the control scheme are simulated in MATLAB platform. The comparison analysis for voltage gain as well as efficiency for the proposed converter and conventional converters are analyzed and the proposed high gain Landsman converter exhibits 97% efficiency in boosting the DC voltage.