Fault analysis in clustered microgrids utilizing SVM-CNN and differential protection

Abstract

The integration of distributed generation, microgrids, and renewable energy sources has significantly enhanced the resilience of modern electrical grids. However, this transition presents challenges in control, stability, safety, and protection due to low fault currents from renewables. This paper addresses these challenges by proposing novel methodologies to enhance fault detection, classification, and localization in microgrids. The literature review highlights a shift towards intelligent learning methods in microgrid protection systems, improving fault response times and identifying electrical faults, including high impedance faults. Nonetheless, existing methods often neglect high impedance fault detection and the integration of differential protection in clustered microgrids. To fill these gaps, this study presents a methodology combining support vector machines and convolutional neural networks for fault detection in microgrids, integrating differential protection for high impedance fault detection. The paper also proposes approaches to optimize protection in clustered microgrid systems. The effectiveness of the methodology is validated using Opal-RT through comparative analyses of signal decomposition techniques, performance and accuracy of support vector machines and convolutional neural networks, KFold validation, and sensitivity analysis. Results demonstrate robustness and high performance, achieving up to 100 % accuracy in fault detection and classification.