Recognition of Single-Phase-to-Ground Fault Section of Distribution Line Based Phase Current Variation

Wei, Yong; Yongbin, Bao; Pengtian, Huang; Shouling, Nie; Shenghai, Han; Pan, Bochao; Ruini, Fan; Xiaohui, Wu

doi:10.1109/icpsasia52756.2021.9621573

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…With an increasingly complex network structure and diverse operational conditions, the system is more susceptible to faults, particularly single-phase grounding faults [1,2]. If single-phase grounding faults are not rectified for a long time, they can easily escalate into two-point or multiple-point grounding short circuits, causing damage to equipment and even posing risks to personal and property safety [3,4]. Timely localization of fault occurrences is essential for rapid risk mitigation, holding significant theoretical and practical importance.…”

Section: Introductionmentioning

confidence: 99%

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

Wang,

Guo,

Shi

2024

Mathematics

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The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks.

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Section: Introductionmentioning

confidence: 99%

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

Wang,

Guo,

Shi

2024

Mathematics

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“…Moreover, Niu et al [7] tackled the challenge of selecting the fault line in resonant grounded distribution networks during single-phase earth faults, suggesting a combination of variational mode decomposition and signal injection as a solution. Yong et al [8] investigated the changes in phase current in different segments of distribution lines before and after single-phase earth faults, developing a segmented identification technique for these faults. A method leveraging the transient characteristics of phase voltage for line fault diagnosis was proposed by Gao et al [9].…”

Section: Introductionmentioning

confidence: 99%

Fault line selection algorithm for single-phase high-impedance ground faults in distribution networks based on the hilbert-huang transform

Liu,

Zhang,

Hang

et al. 2024

J. Phys.: Conf. Ser.

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High-impedance faults in distribution networks have stable characteristics, making them difficult to identify. This paper proposes a fault line selection algorithm for single-phase high-impedance ground faults in distribution networks based on the Hilbert-Huang Transform. The Hilbert-Huang Transform analysis method is used to distinguish fault lines. The first intrinsic mode function component obtained from the decomposition of the zero-sequence current within several cycles after the fault is subjected to a Hilbert Transform, yielding the HHT spectrum peak values. These are then combined with the peak values of the zero-sequence current to construct a two-parameter fault line selection criterion. Finally, simulation data of a single-phase ground fault model in a distribution network verify the effectiveness of the proposed fault line selection method.

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Recognition of Single-Phase-to-Ground Fault Section of Distribution Line Based Phase Current Variation

Cited by 2 publications

References 4 publications

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

Fault line selection algorithm for single-phase high-impedance ground faults in distribution networks based on the hilbert-huang transform

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