High Impedance Fault Detection Protection Scheme for Power Distribution Systems

Protection schemes are used in safe-guarding and ensuring the reliability of an electrical power network. Developing an effective protection scheme for high impedance fault (HIF) detection remains a challenge in research for protection engineers. The development of an HIF detection scheme has been a subject of interest for many decades and several methods have been proposed to find an optimal solution. The conventional current-based methods have technical limitations to effectively detect and minimize the impact of HIF. This paper presents a protection scheme based on signal processing and machine learning techniques to detect HIF. The scheme employs the discrete wavelet transform (DWT) for signal decomposition and feature extraction and uses the support vector machine (SVM) classifier to effectively detect the HIF. In addition, the decision tree (DT) classifier is implemented to validate the proposed scheme. A practical experiment was conducted to verify the efficiency of the method. The classification results obtained from the scheme indicated an accuracy level of 97.6% and 87% for the simulation and experimental setups. Furthermore, we tested the neural network (NN) and decision tree (DT) classifiers to further validate the proposed method.

Automated summary

Protection schemes are essential for ensuring the reliability of an electrical power network. The detection of high impedance faults (HIF) has been a challenge for protection engineers, and this paper proposes a scheme based on signal processing and machine learning techniques to effectively detect HIF. Experimental results show a high accuracy level of 97.6% and 87% for simulation and experimental setups, respectively.