GIS mechanical state identification and defect diagnosis technology based on self‐excited vibration of assembled circuit breaker

Ma, Hongzhong; Liu, Baowen; Xu, Huiying; Chen, Bingbing; Ju, Ping; Zhang, Li; Qu, Bin

doi:10.1049/iet-smt.2018.5578

Cited by 24 publications

(7 citation statements)

References 30 publications

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“…The partial discharge phenomenon mainly occurs in highvoltage electrical equipment, and the discharge phenomenon that occurs within the insulating device in the area and does not penetrate the voltage-applying conductor is called partial discharge [1,2]. It is different from the flashover breakdown phenomenon, which is mainly due to the deterioration of the insulation, resulting in a small localized breakdown.…”

Section: Introductionmentioning

confidence: 99%

Partial discharge fault identification method for GIS equipment based on improved deep learning

Hu,

Li,

Liu

et al. 2024

The Journal of Engineering

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Aiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.

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Section: Introductionmentioning

confidence: 99%

Partial discharge fault identification method for GIS equipment based on improved deep learning

Hu,

Li,

Liu

et al. 2024

The Journal of Engineering

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“…To ensure normal power system operation, strengthening the detection of mechanical faults in HVCBs is essential, and one of the important steps is the detection of vibration signals [4]. Considerable information about the mechanical state is included in the vibration signals resulting from mechanical friction and vibration during the opening and closing of HVCBs, so detecting the mechanical condition of circuit breakers using nonintrusive vibration signals has been extensively researched [5,6]. Mechanical vibration signals are used to diagnose HVCB mechanical failure in this study.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

Xu,

Yan,

Sui

et al. 2024

Applied Sciences

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High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult, so it poses challenges in meeting performance requirements for mechanical fault diagnosis. In this study, a HVCB fault diagnosis method is introduced. It utilizes a combination of grey wolf optimization (GWO) and multi-grained cascade forest (gcForest) algorithms to resolve these issues and improve the accuracy of HVCB mechanical fault diagnosis. To simplify the original vibration signal, the input feature quantity for the fault diagnosis method is obtained by calculating the energy entropy of the wavelet packet decomposition. The GWO algorithm is employed to optimize the parameters of the gcForest model, leading to identification of the optimum parameter configuration. Subsequently, the diagnostic effect in the case of a small sample size was analyzed through a VS1 vacuum circuit breaker example, and the accuracy reached 95.89%. In the case of unbalanced samples, further analysis and comparison with different methods confirm the feasibility and efficiency of the combination of GWO and gcForest algorithms. This study provides an effective solution for the diagnosis of mechanical faults in HVCBs.

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“…Mechanical vibrations that occur in HVCBs during the current interruption operation can be collected by installing acceleration sensors on the HVCB housing. The obtained vibration signals are then used to identify the mechanical state of the HVCB [ 1 , 2 , 3 ]. In this process, due to the influence of the HVCB’s flow size, field noise and the complex structure of the HVCB itself, the acquired vibration signal may be distorted, which increases the difficulty of finding an accurate and efficient contact state recognition model [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

SF6 High-Voltage Circuit Breaker Contact Status Detection at Different Currents

Guo

Han

et al. 2022

Sensors

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Currently, the online non-destructive testing (NDT) methods to measure the contact states of high-voltage circuit breakers (HVCBs) with SF6 gas as a quenching medium are lacking. This paper aims to put forward a novel method to detect the contact state of an HVCB based on the vibrational signal. First, for a 40.5-kV SF6 HVCB prototype, a mechanical vibration detection system along with a high-current generator to provide the test current is designed. Given this, vibration test experiments are carried out, and the vibration signal data under various currents and corresponding contact states are obtained. Afterward, a feature extraction method based on the frequency is designed. The state of the HVCB contacts is then determined using optimized deep neural networks (DNNs) along with the method of adaptive moment estimation (Adam) on the obtained experimental data. Finally, the hyperparameters for the DNNs are tuned using the Bayesian optimization (BO) technique, and a global HVCB contact state recognition model at various currents is proposed. The obtained results clearly depict that the proposed recognition model can accurately identify five various contact states of HVCBs for the currents between 1000 A and 3500 A, and the recognition accuracy rate is above 96%. The designed experimental and theoretical analysis in our study will provide the references for future monitoring and diagnosis of faults in HVCBs.

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GIS mechanical state identification and defect diagnosis technology based on self‐excited vibration of assembled circuit breaker

Cited by 24 publications

References 30 publications

Partial discharge fault identification method for GIS equipment based on improved deep learning

Partial discharge fault identification method for GIS equipment based on improved deep learning

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

SF6 High-Voltage Circuit Breaker Contact Status Detection at Different Currents

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