High Impedance Fault Detection and Isolation in Power Distribution Networks using Support Vector Machines

TL;DR

This paper presents a machine learning-based approach using support vector machines and data analysis techniques for accurate detection and localization of high impedance faults in power distribution networks, tested on IEEE standards.

Contribution

It introduces a novel combination of PCA, Fisher Discriminant Analysis, and SVM algorithms for high impedance fault detection and localization in power systems.

Findings

Multiclass SVM achieves the highest accuracy in fault detection and location.

The proposed methods are fast and reliable across different load conditions.

Machine learning techniques improve fault diagnosis in distribution networks.

Abstract

This paper proposes an accurate High Impedance Fault (HIF) detection and isolation scheme in a power distribution network. The proposed schemes utilize the data available from voltage and current sensors. The technique employs multiple algorithms consisting of Principal Component Analysis, Fisher Discriminant Analysis, Binary and Multiclass Support Vector Machine for detection and identification of the high impedance fault. These data driven techniques have been tested on IEEE 13-node distribution network for detection and identification of high impedance faults with broken and unbroken conductor. Further, the robustness of machine learning techniques has also been analysed by examining their performance with variation in loads for different faults. Simulation results for different faults at various locations have shown that proposed methods are fast and accurate in diagnosing high impedance faults. Multiclass Support Vector Machine gives the best result to detect and locate High Impedance Fault accurately. It ensures reliability, security and dependability of the distribution network.