High impedance fault detection in distribution systems using DWT and Artificial Neural Networks
1
Department of Electrical Power Techniques Engineering, Technical Engineering College, Northern Technical University, Mosul, Iraq
Submission date: 2026-04-19
Final revision date: 2026-05-16
Acceptance date: 2026-05-18
Online publication date: 2026-05-19
Publication date: 2026-05-19
Corresponding author
Abdulrahman Khudhur Ameen
Department of Electrical Power Techniques Engineering, Technical Engineering College, Northern Technical University, Mosul
Department of Electrical Power Techniques Engineering, Technical Engineering College, Northern Technical University, Mosul
KEYWORDS
Artificial Neural NetworkDiscrete Wavelet TransformFault ClassificationHigh Impedance FaultsDistribution System Protection
TOPICS
ABSTRACT
In distribution systems, detection of high impedance faults can be challenging due to the low magnitude, nonlinear and intermittent nature of such faults and the fact that these faults can closely resemble load variations. In this paper, a hybrid approach of DWT and ANN is proposed to accurately identify a HIF in real time. The key contribution is the selective extraction of the DWT detail coefficients D3 and D4 that provide a compact and discriminative feature set that can capture high- and mid-frequency arcing characteristics. The features are classified by a light weight ANN to make fast and reliable decision making. The model is trained and tested with 10,000 labelled samples with an overall accuracy of 98.7%, sensitivity of 97.9%, specificity of 99.5% and false positive rate of 0.5%. The response time of the system is also average at 31.4 ms, which is approximately 1.5 cycles of 50 Hz system. Under noisy conditions, it continues to function well at a 20 dB SNR. By comparing with the traditional algorithm DWT-SVM and fuzzy logic methods, the proposed DWT-ANN method is a practical, efficient and implementable approach, which is suitable for the real-time HIF detection in modern distribution network
FUNDING
This research received no external funding.
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