An Intelligent Detection of High-Impedance Faults for Distribution Lines Integrated With Distributed Generators

Chaitanya, Bokka Krishna; Yadav, Anamika; Pazoki, Mohammad

doi:10.1109/jsyst.2019.2911529

Cited by 53 publications

(27 citation statements)

References 27 publications

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“…Owing to the richness and stability of transient fault signal features, the detection methods employed in RG systems are mainly the passive detection based on transient signals. Primary examples of detection methods employing these features include the wavelet transform (WT) [7]- [10], Hibert-Huang transform (HHT) [11], [12], S-transform (ST) [13]- [18], Prony algorithm [19], [20], mathematical morphology (MM) [21]- [23], fuzzy c-means (FCM) clustering [24], [25], and support vector machine (SVM) [26], [27]. Each of these techniques have pros and cons as listed in Table 1.…”

Section: A Previous Related Workmentioning

confidence: 99%

“…An SVM classification algorithm has been employed for single-phase arc-fault detection in distribution networks based on a combination of transient zero-sequence current and two-phase current difference characteristics [26]. Chaitanya et al [27] presented an high-impedance faults detection technique for distribution lines incorporating the distributed generators, where variational mode decomposition was used to obtain the analytic signal and then used this data as the input into the support vector machine to create an intelligent classifier.…”

Section: A Previous Related Workmentioning

confidence: 99%

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Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

et al. 2020

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Fault detection in resonant grounding (RG) distribution networks remains a challenge due to weak fault signals, extremely complex fault conditions, and unstable intermittent arc grounding faults. This paper addresses this issue by applying generalized S-transform (GST) with a variable factor to conduct denoising of transient zero-sequence currents based on threshold filtering followed by time-frequency distribution filtering in sequence. Meanwhile, this paper proposes a comprehensive multi-criteria faulty feeder detection method based on the transient zero-sequence current polarity (criterion 1), the energy relative entropy (criterion 2), and the total transient current energy (criterion 3). Here, criteria 2 and 3 are based on the time-frequency representation of the GST. The performances of the proposed denoising and faulty feeder detection methods are evaluated under single line to ground faults based on simulations conducted using a modeled 10 kV RG networks with overhead and cable mixed lines in addition to reasonably sophisticated permanent and intermittent arc discharge models to ensure that the simulations faithfully represent actual complex working conditions. Compared with existing method, simulation experiments and field test show that the method proposed in this paper provide a better denoising effect with stronger self-adaptability, higher detection accuracy, and a faster calculation speed. INDEX TERMS Fault detection, resonant grounding distribution networks, generalized S-transform, variable factor, denoising method, intermittent arc grounding fault, multi-criteria.

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Section: A Previous Related Workmentioning

confidence: 99%

Section: A Previous Related Workmentioning

confidence: 99%

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

et al. 2020

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“…This is due to the fact that these faults do not significantly affect the voltage and current characteristics. Indeed, the effect of these faults might be seen as an additional load [5]. Nonetheless, the detection of these faults is of high importance as they could cause serious safety risks and could adversely affect the quality of power supply.…”

Section: Introductionmentioning

confidence: 99%

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

et al. 2021

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Accurate and fast fault detection in transmission lines is of high importance to maintain the reliability of power systems. Most of the existing methods suffer from false detection of high-impedance faults. In this paper, the transfer function (TF) method is introduced to evaluate the effect of impedance and location of faults by analyzing the voltage and current signals in the frequency domain. Interpretation of the results of the TF method is considered as a weakness of this method. In order to alleviate this problem, a convolutional neural network (CNN) and the hybrid model of deep reinforcement learning (DRL) are utilized to identify and locate single-phase to ground short circuit faults in transmission lines. Single-phase to ground short circuit faults with various fault impedances are applied on an IEEE standard transmission line system. Then, the TF traces are calculated and are collected as input datasets for the proposed models. The fault location results for each network are evaluated via various statistical performance metrics such as correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE). The R-value of the CNN and DRL models in fault identification is presented as 96.12% and 98.04%, respectively. Finally, in the early detection of single-phase to ground short circuit fault location (high impedance), the results revealed the efficiency of the DRL model with R=96.61% compared to CNN with R=95.21%. INDEX TERMSTransmission line faults, single-phase to ground short circuit, transfer function, deep reinforcement learning, convolutional neural network.

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“…Moreover, undetected HIFs have been reported to cause instability of renewable energy systems [7]. As a result, reliably detecting and clearing HIFs in a timely manner are crucial to ensure the safety of personnel and maintain the power system integrity [4][5][6].…”

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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An Intelligent Detection of High-Impedance Faults for Distribution Lines Integrated With Distributed Generators

Cited by 53 publications

References 27 publications

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

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