Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

Rai, Khushwant; Hojatpanah, Farnam; Ajaei, Firouz Badrkhani; Grolinger, Katarina

doi:10.3390/en14123623

Cited by 39 publications

(12 citation statements)

References 40 publications

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“…Figure 3 shows the comparison between the current waveform of the normal load and the waveform during HIF. Unlike the simulated cases used in previous publications [10], [13], the load waveform is distorted and dynamic. As a result, distinguishing the HIF from the normal load is more challenging since the magnitudes of distortion in the two cases are similar.…”

Section: A Datasetmentioning

confidence: 94%

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Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Liu¹,

Razeghi-Jahromi²,

Stoupis³

2023

Preprint

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Detection of high impedance faults (HIF) has been one of the biggest challenges in the power distribution network. The low current magnitude and diverse characteristics of HIFs make them difficult to be detected by over-current relays. Recently, data-driven methods based on machine learning models are gaining popularity in HIF detection due to their capability to learn complex patterns from data. Most machine learning-based detection methods adopt supervised learning techniques to distinguish HIFs from normal load conditions by performing classifications, which rely on a large amount of data collected during HIF. However, measurements of HIF are difficult to acquire in the real world. As a result, the reliability and generalization of the classification methods are limited when the load profiles and faults are not present in the training data. Consequently, this paper proposes an unsupervised HIF detection framework using the autoencoder and principal component analysis-based monitoring techniques. The proposed fault detection method detects the HIF by monitoring the changes in correlation structure within the current waveforms that are different from the normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate that the proposed method outperforms the commercially available HIF detection technique while maintaining high security by not falsely detecting during load conditions.

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Section: A Datasetmentioning

confidence: 94%

“…However, their applications for HIF detection are still limited. In recent years, Rai et al [13] applied a convolutional autoencoder trained with simulated HIF scenarios. Then cross-correlation between the reconstructed signal and the original signal was used to discriminate HIFs from loads.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Liu¹,

Razeghi-Jahromi²,

Stoupis³

2023

Preprint

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“…Over the past few decades, various approaches have been examined to remedy these problems [4][5][6][7][8][9][10][11][12][13][14][15]. Typically, harmonic patterns are utilized to capture fault characteristics such as magnitude and angles or harmonics [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…In [10][11][12], the time domain reflectometry-based fault location method using a wavelet decomposition was proposed. Recently, various methods based on machine learning have been proposed [13][14][15]. A hierarchical classification and machine learning method were designed to detect and classify the line-to-ground and line-to-line faults in PV systems in [13].…”

Section: Introductionmentioning

confidence: 99%

“…A hierarchical classification and machine learning method were designed to detect and classify the line-to-ground and line-to-line faults in PV systems in [13]. As a deep learning method, the convolutional autoencoder framework for HIF detection was proposed in [14]. The useful features with advanced machine learning methods were extracted in [15].…”

Section: Introductionmentioning

confidence: 99%

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Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks

et al. 2022

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In power distribution networks, there are many practical fault cases such as high impedance faults, faults and so on. Especially when the faults with electric arc persist, it is dangerous for human beings and circumstances. Nevertheless, it is difficult to classify faults due to various customer load conditions or the presence of distributed energy resources. In this paper, we propose a new mathematical arc model based on experimental event data. For implementing the arc phenomenon, wet gravel was used as a contact conductor after the fault. The experimental results validate the arc transient model. Then, the simulations were performed to verify the success of arc modeling using Kizilcay’s arc model as a comparison method. Finally, we developed a fault detector using the kurtosis detection method, and power system simulations were conducted to evaluate fault detection performance using Matlab/Simulink.

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A novel graph search and machine learning method to detect and locate high impedance fault zone in distribution system

Joga

Sinha

Maharana

2022

Engineering Reports

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High impedance fault (HIF) is difficult to detect by conventional overcurrent protection relays due to the lower fault current values, which are normally lower than the normal current. A fast and reliable algorithm is required to detect this type of fault. This paper proposes a novel method for detecting the location of HIF fault zone in a distribution system by using a novel graph theory‐based zone detection technique along with a Random Search Multilevel Support Vector Machine (RSMSVM) algorithm to classify the faulted zone. Due to shift in‐variance property of “Dual Tree Complex Wavelet Transform (DTCWT),” which has been used, in this paper, to decompose the voltage/current waveform to collect the signature of the signals and feed to the optimized RSMSVM model for classifying fault zone. The proposed method is evaluated on the IEEE 33‐bus system and also IEEE 39 bus test system under normal and noisy conditions. The proposed method is also evaluated for distribution network with the integration of distributed generation.

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Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

Cited by 39 publications

References 40 publications

Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks

A novel graph search and machine learning method to detect and locate high impedance fault zone in distribution system

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