Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders
Open Access
- 18 June 2021
- Vol. 14 (12), 3623
- https://doi.org/10.3390/en14123623
Abstract
High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise.Funding Information
- Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-06222, RGPIN-2017-04772)
This publication has 38 references indexed in Scilit:
- High-Impedance Fault Detection in the Distribution Network Using the Time-Frequency-Based AlgorithmIEEE Transactions on Power Delivery, 2014
- High impedance fault detection methodology using wavelet transform and artificial neural networksElectric Power Systems Research, 2011
- High Impedance Fault Detection Based on Wavelet Transform and Statistical Pattern RecognitionIEEE Transactions on Power Delivery, 2005
- Decision Tree-Based Methodology for High Impedance Fault DetectionIEEE Transactions on Power Delivery, 2004
- An adaptive high and low impedance fault detection methodIEEE Transactions on Power Delivery, 1994
- A practical protective relay for down-conductor faultsIEEE Transactions on Power Delivery, 1991
- Radial distribution test feedersIEEE Transactions on Power Systems, 1991
- Unique aspects of distribution system harmonics due to high impedance ground faultsIEEE Transactions on Power Delivery, 1990
- High impedance fault arcing on sandy soil in 15 kV distribution feeders: contributions to the evaluation of the low frequency spectrumIEEE Transactions on Power Delivery, 1990
- Analysis of high-impedance fault generated signals using a Kalman filtering approachIEEE Transactions on Power Delivery, 1990