Research on a method for GIS partial discharge pattern recognition based on polar coordinate map

Xue, Jun; Zhang, Xiaolan; Qi, Weidong; Huang, Guoqiang; Niu, Bo; Wang, Jing

doi:10.1109/ichve.2016.7800595

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…These methods depend largely on feature engineering, so the quality of features directly affects their effectiveness in PD recognition. The existing feature construction methods mainly include Fourier transforms, wavelet transforms, empirical mode decomposition, S-parameter transformation, and polar coordinate transformation [10]- [13]. Furthermore, in order to effectively extract the most critical characteristic parameters of the PD and reduce the feature dimension, principal component analysis (PCA) and an auto-encoder have been introduced for GIS PD recognition and classification [14], [15].…”

Section: Introductionmentioning

confidence: 99%

A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application

et al. 2019

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With the construction and promotion of the Ubiquitous Power Internet of Things (UPIoT), it is an increasingly urgent challenge to comprehensively improve the recognition accuracy of the gasinsulated switchgear (GIS) partial discharge (PD), and to incorporate the model into UPIoT intelligent terminals supported by edge computing in embedded systems. Therefore, this paper proposes a novel MobileNets convolutional neural network (MCNN) model to identify the GIS PD patterns. We first construct the PD pattern recognition classification datasets by means of experiments and FDTD simulation, and also preprocess images via binarization processing. After constructing the MCNN model, depthwise separable convolutions and an inverse residual structure are adopted to deal with the vanishing gradient of the deep convolutional neural network (DCNN) in the GIS PD pattern recognition process. Then, through the graphics standardization process, the MCNN model is trained and tested. The whole training process is visualized by Tensorboard. Compared with other deep learning models and traditional machine learning methods, MCNN particularly stands out in recognition accuracy and time consumption with a 96.5% overall recognition rate and merely 7.3 seconds in training time. This research explores how to optimize the model by improving the recognition accuracy, and by reducing its computing load, storage space and energy consumption for better incorporation into intelligent terminals in the UPIoT context. INDEX TERMS Gas-insulated switchgear, mobilenets convolutional neural network model, partial discharge, pattern recognition, ubiquitous power Internet of Things.

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Section: Introductionmentioning

confidence: 99%

A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application

et al. 2019

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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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Applications of support vector machine and improved k-Nearest neighbor algorithm in fault diagnosis and fault degree evaluation of gas insulated switchgear

Zheng

Diao

et al. 2017

2017 1st International Conference on Electrical Materials and Power Equipment (ICEMPE)

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Research on a method for GIS partial discharge pattern recognition based on polar coordinate map

Cited by 4 publications

References 2 publications

A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application

A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application

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

Applications of support vector machine and improved k-Nearest neighbor algorithm in fault diagnosis and fault degree evaluation of gas insulated switchgear

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