Adaptive Neuro Fuzzy Inference System Based Intelligent Control for Grid Connected Hybrid Energy System with Improved SEPIC Converter

Abstract

The primary objective of this study is to meet the energy demands of the power consumer through the implementation of Hybrid Renewable Energy System (HRES) with multiple solar panels and wind turbines. As the photovoltaic (PV) power generation is enveloped with multiple advantageous measures like low maintenance, environmental-friendliness and fuel-efficient, it is significantly preferred in this study. However, the low power conversion quality of PV weakens the overall system and so the DC-DC improved SEPIC converter with magnetic coupling is employed to achieve maximum DC output voltage. In addition, the Adaptive Neuro Fuzzy Inference System (ANFIS) is implemented in this work totrack the maximum power from PV and to maximize the energy efficiency in an optimal manner. The MATLAB Simulink is used to validate the present study with optimal outcomes and the obtained results prove that the present approach delivers lesser THD of 1.00%, which in turn efficiently enhances the overall performance of the system. Thus, the research findings of this system are well suited to be applied as the solution for rectifying the issues in the DC link voltage control and grid compensation or synchronization.