Machine Learning Approaches for Fault Detection in Renewable Microgrids

Abstract

This paper presents a novel use of machine learning techniques for identifying faults in renewable microgrids within the field of decentralized energy systems. The study investigates the effectiveness of machine learning models in identifying abnormalities in dynamic and variable microgrid environments. It utilizes a comprehensive dataset that includes parameters such as solar, wind, and hydro power generation, energy storage status, and fault indicators. The investigation demonstrates a notable 94% precision in identifying faults, highlighting the superiority of machine learning compared to conventional rule-based approaches, which attained an accuracy rate of 80%. The precision and recall measures emphasize the well-balanced performance of the machine learning models, reducing both false positives and false negatives, and guaranteeing precise problem detection. The effect of faults on microgrid efficiency is significantly reduced, with an only 2% decrease recorded under fault situations, demonstrating the models’ ability to maintain an efficient energy supply. A comparative study reveals a 14% improvement in accuracy when compared to conventional techniques, emphasizing the benefits of adaptive and data-driven approaches in identifying intricate fault patterns. The sensitivity study validates the resilience of the machine learning models, demonstrating their capacity to adjust to different settings. The practical application of the models is validated by real-world testing in a simulated microgrid environment, which leads to their repeated improvement and improved performance. Ethical concerns play a crucial role in assuring ethical data use during research, particularly in the implementation of machine learning, by upholding privacy and security requirements. The study results indicate significant implications for identifying faults in renewable microgrids, providing a potential opportunity for the progress of robust and sustainable decentralized energy networks. The effectiveness of machine learning models stimulates further study in expanding their deployment for varied microgrid situations, including more machine learning approaches, and resolving obstacles associated with real-time application in operational settings.