Enhancing Power System Reliability Through Advanced Fault Diagnosis Methods: A Deep Learning Approach

Abstract

In order to attain high reliability in power systems, effective fault detection techniques that can quickly identify and mitigate defects must be developed. In this article, we suggest a novel method for improving power system dependability by using deep learning techniques to identify faults. In order to achieve precise fault identification and classification, our technology uses deep neural networks to automatically learn and extract features from power system data. We thoroughly test our method on a range of fault scenarios and real-world datasets to determine its efficacy. The outcomes show how well our approach performs in comparison to conventional fault identification methods, underscoring the possibility of a major increase in power system reliability. By providing useful insights for improving problem diagnosis techniques in power engineering, this research advances system resilience and minimizing downtime.