Fault Detection for High-Voltage Circuit Breakers Based on Time–Frequency Analysis of Switching Transient $E$ -Fields

Zhang, Hongye; Yi, Tongqiang; Kong, Xu; Cheng, Lin; Liu, Haojun

doi:10.1109/tim.2019.2913061

Cited by 23 publications

(6 citation statements)

References 23 publications

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“…This section demonstrates the application of the proposed method to a real experimental HVCB fault test. First, in section 3.1, an experimental setup of HVCB is established, and an FD is defined as {L 1 , L 2 ,…, L 7 }, where L i represents the ith label of the HVCB state. Based on the proposed RF Fatigued opening spring case L 4 Leaking oil of damper case L 5 Damping increase for transmission shaft case L 6 Worn shaft pin case L 7 Loose anchor screws case for BPA, three diagnostic evidences can be produced from the vibration information of three measurement positions.…”

Section: Resultsmentioning

confidence: 99%

“…With the development of intelligent detection technology for electrical equipment, as the core equipment for power protection and safety, the status monitoring and management of high-voltage circuit breakers (HVCBs), which play a key * Author to whom any correspondence should be addressed. role in power protection and safety, have become important topics [1,2]. As two typical measurement signals, the contact stroke [3,4] and coil current [5,6] information have been widely analyzed and applied by many researchers in fault identification of HVCBs.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a multi-sensor diagnostic technology is mainly applied to solve complex scenarios with different prediction results, such as Ex.2 and Ex.3, as shown in figure 1. Ex.2 and Ex.3 show a predicted vector [L i , L j , L k ], which are [1,3,4] and [1,4,4], respectively. Clearly, the conventional voting method is invalid.…”

Section: Introductionmentioning

confidence: 99%

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A novel multi-information decision fusion based on improved random forests in HVCB fault detection application

Xin³

et al. 2022

Meas. Sci. Technol.

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Evaluating the mechanical state of high-voltage circuit breakers (HVCBs) based on vibration information has currently become an important research direction. In contrast to the unicity of the travel–time and current–time curves, the vibration information from the different positions is diverse. These differences are often overlooked in HVCB fault identification applications. Additionally, the fault recognition results based on different location information often vary, and conflicting diagnosis results directly cause the accurate identification of the fault type to fail. Therefore, in this paper, a novel multi-information decision fusion approach is proposed based on the improved random forest (RF) and Dempster-Shafer evidence theory. In the proposed method, the diagnostic distribution of all classification regression trees (CART) in the RF is considered to solve the conflicts among the multi-information diagnosis results. Experimental results show that the proposed method eases the contradiction of multi-position diagnostic results and improves the accuracy of fault identification. Furthermore, compared to the common classifiers and probability generation methods, the effectiveness and superiority of the proposed method are verified.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel multi-information decision fusion based on improved random forests in HVCB fault detection application

Xin³

et al. 2022

Meas. Sci. Technol.

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“…A broadband micro-strip antenna has been employed as the new diagnostic sensor to evaluate the degradation of contacts through establishment of the correlation between the arc duration and signal energy and radiated wave in [49]. In another effort [59], the correlation between different working status of the CBs and the timefrequency characteristics of switching transient E-fields have been addressed to early predict the insulation defect of the HV CBs. In addition, references [54] and [60] have attempted to develop a frequency-based condition assessment approach.…”

Section: F Future Viable Visionsmentioning

confidence: 99%

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Razi-Kazemi

Niayesh

2021

IEEE Trans. Power Delivery

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High voltage circuit breakers (HVCBs) play a critical role on providing the desired reliability, and resiliency in power systems. In order to extend their lifetime and predict the failures, various maintenance policies could be applied on these critical components. Amongst these strategies, condition-based maintenance (CBM) provides a satisfactory agreement with future smart environment. This paper aims to provide an insight into the relevant developments in this subject and to explore the viable visions compatible with future research stream. Accordingly, three directions, i.e. diagnostic signals, intelligent modelling and using monitoring data in asset management have been addressed in this paper. It presents challenges dealing with real-time assessment of the diagnostic signals relating to measurements, and analyses. Subsequently, the issues associated with using artificial intelligent (AI) and Machine learning for providing intelligent algorithms have been discussed. Finally, the connection between the monitoring data and the asset management approach is investigated. The latter is looking for the subjects including remaining lifetime estimation, prioritization, and health index definitions. This paper has attempted to make a bridge from past to future research trends in the failure diagnosis of HVCBs.

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“…Fei et al [9], [10] compared the time-frequency features of the radiated electric fields generated by the circuit breaker (CB) and DS operations and analyzed their differences in terms of mechanism. Zhang et al [11] proposed a non-invasive approach for high-voltage CB diagnosis, which is based on the timefrequency analysis of STEF outside the enclosure. The correlation between the different working status of CB and the time-frequency characteristics of their STEF is established.…”

Section: Introductionmentioning

confidence: 99%

Insulation Fault Diagnosis of Disconnecting Switches Based on Wavelet Packet Transform and PCA-IPSO-SVM of Electric Fields

Zhang

Kong

2020

IEEE Access

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Previous studies have shown that switching operations of gas insulated substations (GIS) can generate transient radiation fields outside the enclosure, namely switching transient electric fields (STEF). The waveform features of STEF can reflect the functioning performance of the switch. To monitor online the working states of disconnecting switches (DS), in this paper, we built an experimental platform to simulate their typical faulty types. Then, under different faulty status, a non-invasive three-dimensional (3D) electric field measurement system was applied to obtain STEF produced by DS. It is difficult for conventional methods to establish an accurate fault-diagnosis model, so we presented a novel method to identify the condition of DS. This innovative approach is based on feature extraction and machine learning and combined signal analysis to classify different defect types of DS. Measured STEF signals were analyzed by the wavelet packet transform(WPT) method in the time-frequency domain, which was transformed to the multi-dimensional feature matrix. The principal component analysis (PCA) algorithm was employed to reduce the dimensionality of the obtained feature matrix, which was also compared to other feature extraction algorithms. In addition, a support vector machine (SVM) with an improved particle swarm optimization (IPSO) algorithm was designed to achieve a PCA-IPSO-SVM model which can be used for signal recognition. The proposed IPSO technique can improve the convergence performance of the PSO through the dynamic adjustment of inertia weight and learning factors. Results show that the proposed fault diagnosis method based on WPT and PCA-IPSO-SVM can effectively identify the insulation faulty signals in STEF. INDEX TERMS Gas insulated substation (GIS); disconnecting switch (DS); switching transient electric field (STEF); wavelet packet transform(WPT); principal component analysis (PCA); support vector machine (SV M); improved particle swarm optimization (IPSO)

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Fault Detection for High-Voltage Circuit Breakers Based on Time–Frequency Analysis of Switching Transient $E$ -Fields

Cited by 23 publications

References 23 publications

A novel multi-information decision fusion based on improved random forests in HVCB fault detection application

A novel multi-information decision fusion based on improved random forests in HVCB fault detection application

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Insulation Fault Diagnosis of Disconnecting Switches Based on Wavelet Packet Transform and PCA-IPSO-SVM of Electric Fields

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