Distribution High Impedance Fault Detection Using the Fault Signal Reconstruction Method

Yao, Weiqiang; Gao, Xiaoqing; Wang, Shanshan; Yang, Lei

doi:10.1109/ei247390.2019.9061798

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Song et al (2022) used Minkowski distance measurement to measure the correlation of wave impedance and construct a fault detection scheme. Yao et al (2019) proposed a feature extraction algorithm to extract scales with the essential fault features and determined the coefficient of the selected scale signal. Routray et al (2015) presented a novel S-transform-based approach to detect the high-impedance fault in the distribution line.…”

Section: Previous and Related Workmentioning

confidence: 99%

Research on the identification of high-resistance ground faults in the flexible DC distribution network based on VMD–inception–CNN

Zheng,

Peng,

Jiang

et al. 2023

Front. Energy Res.

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With the rapid development of flexible DC distribution networks, fault detection and identification have also attracted people’s attention. High-resistance grounding fault poses a great challenge to the distribution network. The fault current is very small and random, which makes its detection and identification difficult. The traditional overcurrent protection device cannot identify and act on the fault current. Therefore, this paper proposes a fault detection method based on variational mode decomposition (VMD) combined with the convolutional neural network (CNN) of the inception module. This method first uses VMD to decompose the positive transient voltage. Second, it inputs the decomposed signal into CNN for training to obtain the optimal parameters of the model. Finally, the model performance is tested based on the PSCAD/EMTDC simulation platform. Experiments show that the detection method is accurate and effective. It can realize the accurate identification of seven different fault types.

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Section: Previous and Related Workmentioning

confidence: 99%

Research on the identification of high-resistance ground faults in the flexible DC distribution network based on VMD–inception–CNN

Zheng,

Peng,

Jiang

et al. 2023

Front. Energy Res.

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show abstract

“…These techniques operate by extracting HIF-related features from the signals. However, the EMD-based method suffers from modal mixing and is sensitive to noise [20]. On the other hand, the VMD-based technique is more robust against noise, nevertheless, it requires a large number of decomposition modes, as well as a high sampling frequency to perform well, which results in a large computational burden.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

et al. 2021

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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.

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Fire probability risk ranking of vegetation species in vegetation high impedance faults

Ozansoy,

Sianaki

2024

International Journal of Electrical Power & Energy Systems

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Distribution High Impedance Fault Detection Using the Fault Signal Reconstruction Method

Cited by 6 publications

References 17 publications

Research on the identification of high-resistance ground faults in the flexible DC distribution network based on VMD–inception–CNN

Research on the identification of high-resistance ground faults in the flexible DC distribution network based on VMD–inception–CNN

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

Fire probability risk ranking of vegetation species in vegetation high impedance faults

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