High-Voltage Circuit-Breaker Insulation Fault Diagnosis in Synthetic Test Based on Noninvasive Switching Electric-Field Pulses Measurement

Kong, Xu; Liu, Haojun; Xie, Yan‐zhao; Guo, Jie; Liu, Qing; Chen, Yuhao; Wang, Shao-Fei; Sun, Xiaojiang

doi:10.1109/tpwrd.2015.2430523

Cited by 23 publications

(8 citation statements)

References 20 publications

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“…The experimental results showed that the method was effective for both known faults with training samples and unknown faults without training samples. Kong et al [7] judged faults using the electromagnetic pulse generated by partial discharge when insulation fault of circuit breaker appeared and verified by experiments that the circuit breaker insulation fault could be distinguished by the characteristics of electromagnetic pulse waveform. This paper briefly introduced the feature extraction method of the vibration signal of high voltage circuit breaker and SVM algorithm and analyzed the high voltage circuit breaker in states of normal operation, fixed screw loosening and falling of opening spring using the SVM algorithm based on the above feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

Xiao

et al. 2019

Int. J. Metrol. Qual. Eng.

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The development of power grid system not only increases voltage and capacity, but also increases power risk. This paper briefly introduces the feature extraction method of the vibration signal of high voltage circuit breaker and support vector machine (SVM) algorithm and then analyzed the high voltage circuit breaker in three states: normal operation, fixed screw loosening and falling of opening spring, using the SVM based on the above feature extraction method. The results showed that the accuracy and precision rates of fault identification of circuit breaker were the highest by using the wavelet packet energy entropy extraction features, the false alarm rate was the lowest, and the detection time was the shortest.

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Section: Introductionmentioning

confidence: 99%

Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

Xiao

et al. 2019

Int. J. Metrol. Qual. Eng.

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“…The signal processing mostly uses the time–frequency analysis method, and the algorithm has a high time and space complexity. Common methods include S-Transform (ST) [ 8 ], Wavelet Transform (WT) [ 9 ], Empirical Mode Decomposition (EMD) [ 10 ], Local Mean Decomposition (LMD) [ 11 ], and Variable Mode Decomposition (VMD) [ 12 ]. ST provides a large number of time–frequency features, but it has a large amount of computation and is easily affected by parameters such as the window width factor [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Huang

2020

Entropy

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The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.

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“…Besides, in practice, CBs only operate when there is a need to control or protect the power system, which means it usually takes a long time only to obtain limited data in terms of both quantity and variety. Sometimes we need to apply artificial faults to CBs in offline experiments, which are irreversible [14]. Thus, it will increase the cost of experiment and even reduce the lifespan of the CBs.…”

Section: Introductionmentioning

confidence: 99%

Typical current modelling and feature extraction of high voltage circuit breaker towards condition analysis and fault diagnosis

Shi

et al. 2020

IET Generation, Transmission & Distribution

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High-Voltage Circuit-Breaker Insulation Fault Diagnosis in Synthetic Test Based on Noninvasive Switching Electric-Field Pulses Measurement

Cited by 23 publications

References 20 publications

Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

Online monitoring and diagnosis of high voltage circuit breaker faults: feature extraction analysis of vibration signals

Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features

Typical current modelling and feature extraction of high voltage circuit breaker towards condition analysis and fault diagnosis

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