Reviews of Research on Mechanical Fault Diagnosis in GIS

Cai, Yuqi; Jin, Liang

doi:10.1051/e3sconf/202015501015

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…While GIS sound signals reflect their operating conditions, they also contain more redundant information. GIS sound signal fault information is mostly concentrated in the low frequency range [43]. In order to make the auditory periphery model more adaptable to the frequency characteristics of GIS sound signals, the number of frequency channels in the auditory saliency model needs to be further adjusted.…”

Section: Characteristic Frequency Adjustment For the Auditory Salienc...mentioning

confidence: 99%

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

Ji,

Liu,

Wang

et al. 2023

Meas. Sci. Technol.

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The mechanical fault of gas-insulated switchgear (GIS) seriously threatens the security of the power grid. Recently, acoustic-based fault diagnosis methods, which have the advantage of non-contact measurement, have been applied to the GIS mechanical fault diagnosis, but vulnerable to the interference of the background noise. To improve the capacity of the acoustic-based GIS fault diagnosis under noise background, by simulating the sound feature extraction ability and anti-noise ability of human auditory system, a novel GIS mechanical fault diagnosis method based on saliency feature of auditory brainstem response (SFABR) is proposed. First, an auditory saliency model, which considers both the auditory periphery and the auditory nerve center was constructed by combining the deep auditory model and the saliency model. After processing GIS emitted acoustic signal, the auditory brainstem response (ABR) was obtained, and the saliency features of the ABR were extracted to obtain the SFABR. Then, the characteristic frequency distribution of the auditory saliency model was adjusted to make it more suitable for the spectral characteristics of the GIS sound signal. Finally, the SFABR was mapped to a two-dimensional CNN to train a model for GIS mechanical fault diagnosis. This method simulates the process of auditory response extraction and the selection effect of auditory attention on sound elements. 110kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

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Section: Characteristic Frequency Adjustment For the Auditory Salienc...mentioning

confidence: 99%

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

Ji,

Liu,

Wang

et al. 2023

Meas. Sci. Technol.

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show abstract

“…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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“…Due to the rise in the partial discharge (PD), the decomposed SF 6 gas inside the GIS reacts with a trace amount of moisture or oxygen at high voltage and high current. And this adversely affects the insulating ability of the GIS, prevents recombination into SF 6 itself, and forms various oxidation by‐products 19,20 . Therefore, insulation deterioration, breakdown, and explosion of GIS due to the increase in these by‐products and PD can have a major effect on human life, industrial safety, and environmental sustainability caused by large scale power outages or fires.…”

Section: Introductionmentioning

confidence: 99%

Digital colorimetric sensing for real‐time gas monitoring for smart green energy system

Cho

Dutta

Kwak

et al. 2023

EcoMat

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In this study, we demonstrate that a digital colorimetric sensor platform can transform a conventional energy system, such as gas‐insulated switchgear (GIS) into a smart green energy system. Our sensor platform consists of a colorimetric sensor film (CSF), an array of silicon photodiodes, and a low‐power‐driven fiber optical photodiode with a wireless communication protocol integrated into an information network. The photodiode measures the transmitted light of the color‐variable CSF when exposed to sulfur dioxide (SO2) gas, a decomposition by‐product of the sulfur hexafluoride (SF6) gas in GIS. We report the electrical photocurrent measurement through the CSF can measure the concentration of SO2 gas via remote and real‐time monitoring and shows a comparable behavior to UV–Vis absorption measurement. The limit of detection of the sensor, 0.78 ppm is sufficient to analyze the GIS insulation deterioration allowing green energy systems.image

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Reviews of Research on Mechanical Fault Diagnosis in GIS

Cited by 3 publications

References 10 publications

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

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

Digital colorimetric sensing for real‐time gas monitoring for smart green energy system

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