Fault identification of high voltage circuit breaker trip mechanism based on PSR and SVM

Ruan, Jiangjun; Wang, Xuezong; Zhou, Taotao; Peng, Xuelin; Deng, Yongqing; Yang, Qiuyu

doi:10.1049/gtd2.12725

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…On the other hand, data-driven methods rely on establishing and training models with a large number of data samples. Ruan J et al [5] introduced a fault identification model based on support vector machines (SVM) for accurate circuit breaker fault identification, leveraging both coil current and vibration signals.…”

Section: Introductionmentioning

confidence: 99%

A two-stage intelligent condition monitoring method for electromagnets in high-voltage circuit breakers

Han,

Fu,

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Intelligent condition monitoring of electromagnets in high-voltage circuit breakers is crucial for the safe operation of power systems. In this study, a hybrid two-phase approach is proposed, aiming at intelligently identifying key feature points in electromagnet current signals by a modified one-dimensional U-Net model, as well as determining the operating state of electromagnets based on the location and magnitude of these key feature points. In the first data-driven phase, an improved U-Net model applicable to time series is introduced for the intelligent identification of key features in the electromagnet current signal. In the second knowledge-based phase, the operating state of the electromagnet is accurately identified based on the position and size of the key feature points by exploring the motion mechanism of the electromagnet. The experimental results show that the proposed method can effectively identify the key points, and the recognition success rate is close to 100%. The proposed method can realize the adaptive identification of various electromagnet faults with only a few fault samples. Therefore, the proposed method paves the way for robust state identification of electromagnets and enjoys the merit of strong anti-interference.

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Section: Introductionmentioning

confidence: 99%

A two-stage intelligent condition monitoring method for electromagnets in high-voltage circuit breakers

Han,

Fu,

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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show abstract

“…The calculation and analysis methods for fault diagnosis of high-voltage circuit breakers mainly include support vector machines 14 , 15 , wavelet analysis 9 , short-term energy analysis 16 , empirical mode decomposition 17 , information entropy 18 , transfer learning approach 19 , etc. Compared to other methods, the support vector machine method has significant advantages in dealing with small sample and nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

Wang,

Zhou,

2024

Sci Rep

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High voltage circuit breakers are one of the core equipment in power system operation, and the voiceprint signals generated during operation contain extremely rich information. This paper proposes a fault identification method for high voltage circuit breakers based on voiceprint information data. Firstly, based on the developed voiceprint information data acquisition device, the voiceprint information of a certain high voltage circuit breaker is obtained; Secondly, an improved S-transform is proposed in the article, which generates an amplitude matrix based on the S-transform of voiceprint information; Then, through the matrix Singular value decomposition method, the fault feature quantity of voiceprint information is extracted from the time–frequency angle, and the diagnosis system of the support vector machine model is established, and the system is trained to realize the fault identification of the high-voltage circuit breaker; Finally, through experimental simulation calculations, it was shown that the accuracy of the proposed fault identification method in different operating conditions reached 92.6%, verifying the good accuracy and robustness of the proposed method and equipment.

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“…Saari et al [ 24 ] trained the model by using the fault features extracted from the vibration signals as the input of one-class SVM and realized the automatic detection and identification of wind turbine bearing faults. Ruan et al [ 25 ] use the phase space reconstruction (PSR) method to extract the feature set that represents the health condition of the trip mechanism from the vibration signal and input it into the fault identification model based on SVM.…”

Section: Introductionmentioning

confidence: 99%

Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm

et al. 2023

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To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units.

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Fault identification of high voltage circuit breaker trip mechanism based on PSR and SVM

Cited by 9 publications

References 27 publications

A two-stage intelligent condition monitoring method for electromagnets in high-voltage circuit breakers

A two-stage intelligent condition monitoring method for electromagnets in high-voltage circuit breakers

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm

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