ADVANCED ADAPTIVE CONTROL OF SOLAR-BATTERY MICROGRIDS USING NEURO-FUZZY INTELLIGENCE

Abstract

As renewable energy resources start to penetrate remote and standalone microgrids, maintaining voltage stability, power quality and reliable energy management has become a big challenge. Solar photovoltaic (PV) systems are environmentally friendly and sustainable; however, they are also intermittent in nature because of the fluctuations of solar irradiance and weather conditions. This project aims for a solution to these challenges by proposing an Intelligent Neuro-Fuzzy Control Strategy for Voltage Stability Enhancement in Standalone Solar–Battery Microgrids. The proposed system consists of an Adaptive Fuzzy–Artificial Neural Network (Fuzzy-ANN) controller, a solar PV generation unit, a battery energy storage system (BESS), a DC–DC boost converter and a Voltage Source Inverter (VSI). This hybrid controller has the advantages of fuzzy logic and adaptive learning and self-tuning of artificial neural networks. The controller continuously regulates the system parameters like load demand, solar irradiance, battery state of charge, and the output voltage and optimizes the control action accordingly to achieve a stable voltage profile and optimum performance of the system. The suggested control method provides better transient response, lowers steady state error, reduces voltage fluctuations, and improves power quality by reducing the Total Harmonic Distortion (THD). The system is modeled and simulated in MATLAB/Simulink for different operating conditions such as load disturbances and fluctuations in renewable energy. The proposed Adaptive Fuzzy-ANN controller is found to be superior to the conventional PI and stand alone fuzzy controllers based on the simulation results in terms of voltage regulation accuracy, recovery time, system reliability and harmonic reduction. Overall, the intelligent control framework is a promising solution for managing a microgrid remotely, providing a reliable and efficient way to ensure sustainable energy use and power supply in off-grid areas.