High-Impedance Fault Detection Method Based on One-Dimensional Variational Prototyping-Encoder for Distribution Networks

Xiao, Qin; Guo, Mei; Chen, Duan-Yu

doi:10.1109/jsyst.2021.3053769

Cited by 35 publications

(11 citation statements)

References 29 publications

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“…The correlation coefficient is used to explain the correlation between two waveforms. The correlation coefficient is determined through human expert experience in four different levels as given in (27). Similar classification of the levels can also be found in [37,38].…”

Section: Principle For Fault Line Selectionmentioning

confidence: 99%

“…So, in the case of high-resistance grounding faults, fault detection is extremely difficult and line selection is difficult [24,25]. For resonant grounding distribution systems [26,27], active distribution networks with neutral grounding via a Petersen coil [27], the nonlinear modeling analysis method [28], nonlinear voltage-current characteristic profile identification [29], the Morlet wavelet transform [30], and empirical wavelet transform and differential faulty energy [31] methods have been proposed. The above methods are practical in ideal networks especially in the IEEE typical bus systems, and simulation analysis is the main approach for feasibility verification, but for real applications, the effectiveness of these methods still needs to be discussed.…”

Section: Introductionmentioning

confidence: 99%

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High-Resistance Grounding Fault Detection and Line Selection in Resonant Grounding Distribution Network

Yang,

Lu,

2023

Electronics

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The detection and selection of fault lines in resonant grounding distribution networks pose challenges due to the lack of sufficient state parameters and data. This paper proposes an approach to overcome these limitations by reconstructing the initial criterion for fault occurrence and fault line selection. Firstly, a combination of 15% of the traditional phase voltage and the sum of the zero-sequence voltage gradient is suggested as the initial criterion for fault occurrence. This improves the speed of the line selection device. Additionally, the transient process of high-resistance grounding in a resonant grounding system is analyzed based on the impedance characteristics of high- and low-frequency lines. The line selection criterion is then established by comparing the current and voltage derivative waveforms on high- and low-frequency lines. To verify the effectiveness of the proposed method, simulations are conducted. The results demonstrate that this method can effectively handle high-resistance grounding faults under complex conditions while meeting the required speed for line selection.

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Section: Principle For Fault Line Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Resistance Grounding Fault Detection and Line Selection in Resonant Grounding Distribution Network

Yang,

Lu,

2023

Electronics

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“…Reference [30] introduces semi supervised learning to achieve fault recognition in unlabeled datasets. Reference [31] uses a variational prototype autoencoder to extract signal features and trained a decision tree to determine fault types. The success of fault diagnosis in transmission lines primarily relies on the alarm information obtained from the monitoring devices stationed at various points in the power system.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Fault Diagnosis Method Based on Improved Multiple SVM Model

Sun,

Liu,

Lin

et al. 2023

IEEE Access

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The accurate diagnosis of transmission line fault types is a prerequisite for quickly removing faults and restoring power supply, as well as the key to effectively reducing user economic losses, ensuring stable operation of the power system. The rapid development of artificial intelligence technology has been a promising way for fault diagnosis. However, the existing methods are still limited by small simples and accuracy of generalization. To overcome these problems, a transmission line fault diagnosis method based on an improved multiple SVM (MSVM) model is proposed in this paper. Firstly, the transmission line was selected as the research object, and its fault types and causes were analyzed in detail. Then, typical fault information are selected and corresponding MSVM model is established. Meanwhile, genetic algorithm (GA) is adopted to optimize model parameters to improve the accuracy of generalization. Finally, an improved IEEE-30 node test system and a real-world testing data are adopted to verify the accuracy and feasibility of the proposed method. Through analysis, fault diagnosis accuracy of the proposed method can be improved by up to 11% with better fitness value. INDEX TERMS Transmission line; fault diagnosis; improved multiple SVM model

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“…In (Silva et al, 2018), an evolving neural network was used with the discrete wavelet transform to identify electrical current patterns. Similarly, the approach proposed in (Xiao et al, 2022) utilized the 1D variational prototyping encoder and decision tree for feature extraction and fault detection. An improved generative adversarial network was proposed in (Guo et al, 2023) to generate sufficient samples for the HIF detection model.…”

Section: Introductionmentioning

confidence: 99%

Application of semantic segmentation in High-Impedance fault diagnosis combined signal envelope and Hilbert marginal spectrum for resonant distribution networks

Gao

Guo

Lin

et al. 2023

Expert Systems with Applications

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High-Impedance Fault Detection Method Based on One-Dimensional Variational Prototyping-Encoder for Distribution Networks

Cited by 35 publications

References 29 publications

High-Resistance Grounding Fault Detection and Line Selection in Resonant Grounding Distribution Network

High-Resistance Grounding Fault Detection and Line Selection in Resonant Grounding Distribution Network

Transmission Line Fault Diagnosis Method Based on Improved Multiple SVM Model

Application of semantic segmentation in High-Impedance fault diagnosis combined signal envelope and Hilbert marginal spectrum for resonant distribution networks

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