Evaluation of Fuzzy-Based Artificial Neural Network for Power Quality Enhancement

Abstract

The rapid integration of renewable and decentralized energy sources is transforming the operation of power networks, leading to challenges in efficient electricity utilization, optimal harnessing of renewable energy, and maintaining stable voltage levels. To tackle these issues, clusters of multi-renewable energy systems are interconnected through electrical converters employing fuzzy controllers. Power quality (PQ) controllers have been developed for various renewable energy sources to meet the growing energy demands. Enhancing power electronics separation based on power storage and distributed generation offers significant opportunities for managing PQ. This research focuses on improving the performance of smart hybrid multi-renewable energy systems using microgrids through the utilization of artificial neural networks (ANNs). The proposed approach incorporates a fuzzy controller-based pulse generation technique for a DC-to-AC inverter, ensuring efficient power conversion while maintaining PQ factors. The fuzzy controller effectively handles stringent constraints on process parameters by leveraging adjustable variables. Hardware evaluation and performance analysis were conducted using a peripheral interface controller (PIC) controller, providing an accurate representation of the results.