Classification of Faults in Microgrids Systam

Abstract

The smart grid can self-heal and isolate a defect to lessen its harmful effects by using fault detection and location. Numerous methods are put out in the literature for the use of artificial intelligence algorithms in the identification and categorization of defects. Based on data from sensors and smart meters put in the smart grid, this research provides a unique approach for defect detection, classification, characterisation, and localization utilizing fuzzy logic and neural networks. The OpenDSS-Matlab platform is used in tandem with the suggested technique in this paper to enable the simultaneous detection and classification of network faults. The suggested approach is validated using the IEEE 37-bus system. With the suggested approach, a good value of 99.9% precision was reached, as reported in the literature. Network operators may find this strategy helpful in identifying, characterizing, and pinpointing issues.