Electrical Fault Detection And Localization Using Machine Learning

Abstract

Electrical faults pose significant challenges in power systems, often leading to operational disruptions and safety concerns. Timely detection and localization of these faults are crucial for ensuring uninterrupted power supply and preventing potential hazards. In this context, machine learning techniques have emerged as promising tools for enhancing fault diagnosis systems. This study presents a comprehensive approach to electrical fault detection and localization, leveraging machine learning algorithms. Drawing inspiration from real-world scenarios, such as power grid failures and industrial equipment malfunctions, we address the pressing need for reliable fault detection methods. The proposed methodology focuses on the utilization of decision tree classifier and contrasts its performance with the random forest algorithm. Through preprocessing steps, including feature engineering and normalization, the data is prepared for training. Additionally, this study addresses the issue of imbalanced classes by employing the Synthetic Minority Over-sampling Technique (SMOTE), which enhances the robustness of our analysis. Using a diverse dataset encompassing various fault types, we conduct extensive experimentation and evaluation. Our results demonstrate the superiority of decision tree-based methodologies in achieving high accuracy rates in fault detection and localization tasks. By presenting practical insights and performance metrics, this research contributes to the ongoing discourse in the field of electrical engineering. Overall, this study not only highlights the effectiveness of machine learning approaches in fault diagnosis but also underscores their potential for real-world implementation in power systems and industrial settings. These findings hold significant implications for engineers and practitioners seeking reliable solutions for mitigating electrical faults and ensuring operational resilience.

Topics & Concepts