Integration of Renewable Energy and Microgrid Systems to Enhance Voltage Quality and Minimize Harmonic Distortion Losses Using Advanced Control Techniques

This study explores integrating renewable energy sources into microgrid systems to improve voltage quality and reduce harmonic distortion losses using an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. Microgrids with renewables offer enhanced energy reliability and efficiency but face challenges like voltage fluctuations and harmonic distortions. Renewable sources like solar and wind introduce variability, impacting voltage stability and causing harmonic distortions in the grid. The ANFIS controller adapts to these dynamics by dynamically adjusting parameters, leveraging neural network adaptability and fuzzy logic's interpretability to manage nonlinear and uncertain behaviors typical of renewables. The research aims to optimize microgrid performance by mitigating voltage fluctuations and harmonic distortions through ANFIS. By improving operational stability and efficiency, this approach supports effective renewable energy integration into broader grid infrastructures. Through empirical analysis and simulations, the study provides insights into ANFIS's practical application in microgrid management, contributing to sustainable energy solutions and grid resilience.This research underscores the importance of ANFIS controllers in enhancing renewable energy integration within microgrid systems, offering actionable strategies for improving energy sustainability and reliability in modern power networks.