Development of non-intrusive monitoring for reactive switching of high voltage circuit breaker

Lopez-Roldan, J.; Pater, Ryszard; Poirier, Sébastien; Birtwhistle, David; Tang, Tao; Doche, Rene; Blundell, Mark

doi:10.1016/j.ijepes.2014.03.048

Cited by 16 publications

(6 citation statements)

References 15 publications

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“…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. Figure 7 indicates how a frequency response is implemented to detect possible malfunction in contacts.…”

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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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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“…These EM pulses have the typical characteristics of a transient pulse signal with a sharp rising edge and an ultra-wide band (UWB); hence, both the time- and frequency-domain measurements pose some difficulties [ 8 , 9 ]. However, as the military, aerospace, transportation, and electronics industries pay increasing attention to electromagnetic interference (EMI) problems, the undistorted measurement of transient EM fields, which serves as the basis of electromagnetic compatibility (EMC) analysis, is becoming more demanding, and related techniques have become a focus of recent research [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Identification and Compensation for D-Dot Measurement System in Transient Electromagnetic Pulse Measurement

Jin

Liu

2022

Sensors

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The measurement of the transient pulsed electromagnetic (EM) field is essential for analyzing electromagnetic compatibility. Due to their good performance, D-dot sensors, combined with numerical integration computation for signal recovery, are commonly used to measure electromagnetic pulses (EMPs). However, the integration approach is occasionally flawed due to a non-ideal frequency response or noise, causing distortions in the reconstructed signal. In order to better understand the dynamic performance of the sensor, a nonlinear Hammerstein model is employed in the system identification for the sensor with the calibration data collected in the laboratory environment. When identifying the linear component based on the ultra-wideband characteristics of the measured transient pulse, a two-step identification approach with two different pulse excitation modes, low frequency and high frequency, is utilized to conduct the modeling across the entire frequency range. Based on the reliable identification and modeling of the D-dot sensor, a compensation system that corresponds to the nonlinear Hammerstein model has been developed for the practical signal recovery of the incident E-field. After compensation, the dynamic characteristics of the sensor are significantly improved, and the system compensation approach outperforms the integration method in signal recovery for the incident E-field.

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“…Operation rejection caused by "mechanical stuck" (i.e., failing to open or close on command) is responsible for the highest proportion of major failures of HVCBs, at 34% of the overall failures [6]. Although typical on-line system monitors can improve the reliability of the mechanism system, the feasibility of these monitors is prevented by the high hardware cost and construction difficulty [7,8]. Thus, the diagnosis of potential faults concealed inside operating mechanisms is meaningful in enhancing the stability of the electrical power supply to consumers.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

et al. 2020

Chin. J. Mech. Eng.

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According to statistic data, machinery faults contribute to largest proportion of High-voltage circuit breaker failures, and traditional maintenance methods exist some disadvantages for that issue. Therefore, based on the wavelet packet decomposition approach and support vector machines, a new diagnosis model is proposed for such fault diagnoses in this study. The vibration eigenvalue extraction is analyzed through wavelet packet decomposition, and a four-layer support vector machine is constituted as a fault classifier. The Gaussian radial basis function is employed as the kernel function for the classifier. The penalty parameter c and kernel parameter δ of the support vector machine are vital for the diagnostic accuracy, and these parameters must be carefully predetermined. Thus, a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c and δ for the support vector machine in each layer are determined by the particle swarm algorithm. The validity of this fault diagnosis model is determined with a real dataset from the operation experiment. Moreover, comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented. The results indicate that the proposed fault diagnosis model yields better accuracy and efficiency than these other models.

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Development of non-intrusive monitoring for reactive switching of high voltage circuit breaker

Cited by 16 publications

References 15 publications

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

Identification and Compensation for D-Dot Measurement System in Transient Electromagnetic Pulse Measurement

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

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