Feature extraction of GIS partial discharge signal based on S‐transform and singular value decomposition

Dai, Dangdang; Wang, Xianpei; Long, Jiachuan; Tian, Meng; Guang, Zhu; Zhang, Jieming

doi:10.1049/iet-smt.2016.0255

Cited by 49 publications

(24 citation statements)

References 27 publications

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“…Compared with the classification methods, feature extraction plays a more important role in pattern recognition, as the quality of the feature directly affects the performance of the classification algorithm. Numerous feature extraction methods involving time-resolved partial discharge (TRPD) and phase-resolved partial discharge (PRPD) have emerged, mainly including Fourier transforms, wavelet transforms, Hilbert transforms, empirical mode decomposition, S-parameter transformation, fractal parameters, and polar coordinate transformation [19][20][21][22][23][24][25]. The identification method based on PRPD mode has strong anti-interference ability; however, the synchronous phase of the high voltage side is not necessarily obtained in the field measurement, and this analysis method is difficult to implement when there is external electromagnetic interference.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

et al. 2019

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Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data.

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Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

et al. 2019

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“…In this study, an electrical method that employs a UHF sensor is used for the PD measurement system. Time-resolved PD (TRPD) and phase-resolved PD (PRPD) analyses have been studied in order to examine the characteristics of PDs in GIS [18][19][20][21][22][23][24][25][26][27]. The TRPD-based method is a method of analyzing the time-domain features of PD pulses, frequency-domain features of PD pulses, and both time-domain and frequency-domain features of PD pulses [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Nguyen

Yun³

et al. 2018

Energies

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The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.

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“…Firstly, GIS demands less area thus reducing the cost; secondly, GIS has a longer overhaul period; and finally, GIS has higher reliability. For these reasons, GIS has been widely used in the world nowadays [1][2][3][4]. However, GIS has a complex structure that internal defects may come into being during process of manufacturing, transferring, and installing [5,6].…”

Section: Introductionmentioning

confidence: 99%

Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

Zeng¹,

Tang²,

Zhang³

et al. 2018

Simulation and Modelling of Electrical Insulation Weaknesses in Electrical Equipment

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Gas-insulated switchgear (GIS) is a common electrical equipment, which uses sulfur hexafluoride (SF 6) as insulating medium instead of traditional air. It has good reliability and flexibility. However, GIS may have internal defects and partial discharge (PD) is then induced. PD will cause great harm to GIS and power system. Therefore, it is of great importance to study the intrinsic characteristics and detection of PD for online monitoring. In this chapter, typical internal defects of GIS and the PD characteristics are discussed. Several detection methods are also presented in this chapter including electromagnetic method, chemical method, and optical method.

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Feature extraction of GIS partial discharge signal based on S‐transform and singular value decomposition

Cited by 49 publications

References 27 publications

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

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