Discrete Wavelet Transform-Based Triggering Method for Single-Phase Earth Fault in Power Distribution Systems

Lin, Cheng; Gao, Wei; Guo, Mei

doi:10.1109/tpwrd.2019.2913728

Cited by 57 publications

(20 citation statements)

References 21 publications

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“…The master and slave systems of the chaotic system are expressed as (8) and (9) , where u=1,2,…, v-2, and v is the total amount of data of the partial discharge signal. Then the dynamic error calculated from (12) and (13) is expressed as (14) in the form of a matrix.…”

Section: B Chaos Synchronization Detection Methodsmentioning

confidence: 99%

“…This paper combined the discrete wavelet transform (DWT) [9]- [11], the Lorenz chaotic system, and a convolutional neural network (CNN) [12] to identify the insulation faults of four types of power cables. During identification, the partial discharge signal detected by HFCT was filtered by discrete wavelet and input into the Lorenz chaotic system to draw the dynamic error scatter diagram, and the image features were extracted and identified by CNN.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

Wang

Liao

2022

IEEE Trans. Power Delivery

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In this paper, the discrete wavelet transform (DWT) and a chaotic system were combined with a convolutional neural network (CNN) and applied to the diagnosis of insulation faults in XLPE (cross-linked polyacetylene) power cables. First, four different types of insulation faults in power cables were constructed, including the normal state of the cable, the short outer semi-conducting layer, impurities in the insulation layer, and insulation layer damage, and a high-speed capture card (NI PXI-5105) was adopted to measure the partial discharge (PD) signal, which was then filtered through discrete wavelet transform. Then, based on the Lorenz chaotic system, a dynamic error scatter diagram was established as the feature of each fault state. Finally, the dynamic error scatter diagram was processed by CNN to recognize four different types of faults in the power cable. The test results show that the method proposed in this paper can quickly recognize the fault state of power cables and has excellent performance in terms of recognition accuracy, which reaches 97.5%. Therefore, the proposed method can effectively detect the fault signal changes of power cables and identify the fault state of power cables in real time.

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Section: B Chaos Synchronization Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

Wang

Liao

2022

IEEE Trans. Power Delivery

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“…The transfer function (TF) method is a comparative test to diagnose defects, especially in transient states. So far, this method has been widely used in power industry, e.g., [34]- [38], including detection of mechanical defects in power transformer windings [34], [39], diagnostic operations in electrical machines [36], and detection of distribution network damages [37], [38], [40]. Since this method examines the changes in a system in the frequency domain, in most literature, this method has been introduced as a frequency response analysis.…”

Section: Transfer Function Methodsmentioning

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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“…W. C. Santos et al [7] used the discrete wavelet transform to monitor highfrequency voltage components and presented a transientbased algorithm for faulty line identification in distribution network. Lin et al [8] proposed a discrete wavelet transform based triggering algorithm, which takes wavelet coefficients variations of the zero-sequence voltage as the judge basis. Guo et al [9] proposed a novel method of fault-section location using the transient zero-sequence currents at doubleends of the line section.…”

Section: Introductionmentioning

confidence: 99%

Faulty Line Identification Method Based on Bayesian Optimization for Distribution Network

Zhu

et al. 2021

IEEE Access

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Selective ground-fault protection is greatly valued for the safe and reliable operation of power systems. With the wide adoption of fault indicator in distribution network, the amount of available fault data increases dramatically. The in-depth investigation of fault recording data helps improve the accuracy of faulty line identification. To perform fault data analysis with higher efficiency, a single-phaseto-ground fault identification model based on the k-Nearest Neighbor (kNN) classification algorithm is proposed in the paper. In this model, the eigenvectors consist of wavelet energy ratio, wavelet coefficients variance and wavelet power obtained by the decomposition of transient components. Furthermore, through the theoretical analysis and experimental comparison of three parameter adjustment algorithms, Bayesian Optimization algorithm is selected to find the optimal parameters of fault identification model, and realize the adaptive adjustment of model parameters. Finally, the validity and feasibility of the model are verified by the experimental data, and the accuracy and efficiency of fault identification are improved by using Bayesian Optimization algorithm.

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Discrete Wavelet Transform-Based Triggering Method for Single-Phase Earth Fault in Power Distribution Systems

Cited by 57 publications

References 21 publications

Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

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

Faulty Line Identification Method Based on Bayesian Optimization for Distribution Network

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