An Intelligent Fault Detection and Classification Scheme for Distribution Lines Using Machine Learning

Abstract

The current paper focuses on the development and deployment of Machine Learning (ML) based algorithms for the classification and detection of different faults in the electrical distribution system. The methodology adapted using ML has higher computational accuracy than traditional computational algorithms. The parameters involved in developing ML for fault detection/classification are fundamental frequency, fault voltage, and current components at fault situations. During faults, the current and voltage waveforms consist of high-frequency transient signals. The Wavelet Decomposition (WD) technique is used to break down transient signals to obtain the required information. To investigate the performance of the ML-based algorithms, an IEEE 33 bus system is utilized, and a fault is generated in Matlab/Simulink environment. The methodologies used for fault detection and classification are K Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM). The performance of the designed algorithm is assessed by employing a confusion matrix, and the results demonstrated extraordinarily high accuracy.

Keywords

• machine learning
• wavelet decomposition
• k nearest neighbor
• decision tree
• support vector machine