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

Ji, Tianyao; Ye, Xiuzhen; Shi, Miao; Li, Mengshi

doi:10.1049/iet-gtd.2018.5385

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…In the existing research, the trip mechanism fault is often diagnosed by the CC. For the usual mechanical faults caused by jam, most studies only consider the open/close latch jam, and ignore the possible jam between the coil and the plunger [13][14][15][16]. Through simulation tests, it is found that when the coil-plunger jam occurs, the features of the CC change little, and it is difficult to identify the faults through the current features.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

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

Ruan

Wang

Zhou³

et al. 2022

IET Generation Trans & Dist

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The trip mechanism is a weakness in circuit breakers. Traditional fault identification based on the coil current is difficult to report early mechanical defects such as coil‐plunger jam. Here, the vibration signal during the trip process was extracted. Based on the coil current signal and vibration signal, the characteristics of the trip mechanism are analyzed. The phase space reconstruction (PSR) method is used to extract features from the vibration signal. Combined with the features from the coil current waveform, the feature set representing the health condition of the trip mechanism is proposed. The fault simulation tests are carried out and the variation of current vibration characteristics under fault conditions is studied. The fault identification model based on a support vector machine (SVM) is proposed and compared with the identification results when features are extracted from a single signal. When the power supply voltage is dispersed, the prediction accuracy of fault identification is 83.3% considering only the features of the current waveform or vibration signal. And the identification accuracy rises to 96.7% while using the feature set of current and vibration signals. On basis of the current signal, the method further combines the vibration signal so that the robustness of defect identification improves.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

“…On the other hand, the open/close CC was taken as the characteristic signal in some researches. Parameters such as time and current corresponding to the inflection point in the CC curve were extracted and used for the fault identification of the trip mechanism [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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

Ruan

Wang

Zhou³

et al. 2022

IET Generation Trans & Dist

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show abstract

“…Their research is based on the vibration signals of circuit breakers, achieving the diagnosis of typical faults during the opening process. Chmielewski et al [15][16][17] conducted research on the modelling of circuit breakers and explored the relationship between fault characteristics and typical signals of circuit breakers. Huang et al [18] used phase space reconstruction to extract vibration signal features based on the coil current and vibration signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

Li,

Zhang,

et al. 2024

ENERGY

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Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into local optimal solutions. The Sparrow Search Algorithm (SSA) with self-adjusting weight factors combined with bidirectional mutations is added to optimize the selection of BPNN hyperparameters. The results show that the ISSA-BPNN can accurately and quickly distinguish six conditions of motor voltage reduction: motor voltage increase, motor voltage decrease, energy storage spring stuck, transmission gear stuck, regular state and energy storage spring not locked. It is suitable for fault diagnosis and detection of the energy storage part of LVCB.

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“…Clustering algorithm is also widely used in the diagnosis of circuit breakers, but simple clustering may lead to unstable fault diagnosis results [43]. In most cases, it is difficult for the objects in the dataset to specify which cluster they belong to, and there is a certain fuzziness.…”

Section: Distribution Of Articles By Key Component In Cold Ironing Sy...mentioning

confidence: 99%

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

Ding

Yao²,

et al. 2022

Sustainability

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Nowadays, cold ironing technology has been demonstrated to be an effective solution to deal with the environmental and social problems brought by port ship emissions and relevant effects. The working states of cold ironing equipment, especially the key components such as circuit breakers, transformers and frequency converters, have a significant effect on the safety and reliability of the whole system. However, due to the harsh working environment of cold ironing equipment, they are prone to a high risk of failure. In this respect, fault diagnosis methods can play a significant role in detecting potential failure in time and guarantee the safe and reliable operation of the cold ironing system. In recent years, research on the fault diagnosis of a cold ironing system has been rapidly growing, and this paper aims to present a comprehensive review of this literature, with an emphasis on the fault diagnosis technology applied to the key components in a cold ironing system. This review classifies the literature according to the type of key component, and, for each special type of component, the fault diagnosis methods are further categorized and analyzed. This paper provides useful references for professionals and researchers working on the fault diagnosis of a cold ironing system and points out valuable research directions in the future.

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

Cited by 12 publications

References 23 publications

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

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

Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

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