Machine learning approaches for fault detection in renewable microgrids

Abstract

This study focuses on investigating and using machine learning (ML) methods to identify faults in renewable microgrids. It highlights the difficulties and intricacies associated with these dynamic energy systems. The examination of real-world data obtained from solar and wind power production, battery storage status, fault signals, and machine learning model performance highlights the complex nature of fault detection techniques in renewable microgrids. An analysis of data on renewable energy production demonstrates oscillations in the outputs of solar and wind power, highlighting differences of about 5-10% across certain time periods, thereby illustrating the intermittent characteristics of renewable energy sources. Simultaneously, the energy stored in batteries inside the microgrid shows a progressive decrease of about 3-5% in stored energy levels across time intervals, indicating possible consequences for the stability of the system. The fault detection signals display erratic patterns, which emphasize the intricacies involved in finding and categorizing issues inside the system. The assessment of machine learning models, which includes both supervised and unsupervised learning methods, reveals many performance measures. Supervised models provide greater accuracy rates, often ranging from 85% to 90%. However, they are prone to occasional misclassifications. In contrast, unsupervised models provide a moderate level of accuracy, often ranging from 75% to 80%. They exhibit flexibility in detecting faults, but their precision is limited. The study highlights the need of using a combination of supervised and unsupervised machine learning models to improve the accuracy of fault detection in renewable microgrids. These results provide valuable understanding of the intricacies and difficulties of fault detection procedures, which may lead to further progress in improving the dependability and durability of renewable microgrid systems.

Keywords

• fault detection
• renewable microgrids
• machine learning
• energy management
• sustainability