GIS partial discharge pattern recognition via deep convolutional neural network under complex data source

Song, Hui; Dai, Jufeng; Sheng, Gehao; Jiang, Xiuchen

doi:10.1109/tdei.2018.006930

Cited by 132 publications

(59 citation statements)

References 25 publications

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“…To effectively solve this problem, deep learning methods that rely on automatic feature extraction are introduced into GIS PD pattern recognition. At present, these deep learning models include LeNet5, AlexNet, one-dimensional convolution, and long short-term memory (LSTM) models [30][31][32][33][34].…”

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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“…gradually begun to be applied in the field of PD pattern recognition [19][20][21]. In 2016, Mingzhe Rong used convolutional neural network for PD pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.In recent years, with the rapid development of recognition technologies in the research fields such as images, texts, and videos, classification algorithms based on deep learning, for example, dbn (deep belief networks), dnn (deep neural networks), cnn (convolutional neural networks), have gradually begun to be applied in the field of PD pattern recognition [19][20][21]. In 2016, Mingzhe Rong used convolutional neural network for PD pattern recognition.…”

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confidence: 99%

“…In recent years, with the rapid development of recognition technologies in the research fields such as images, texts, and videos, classification algorithms based on deep learning, for example, dbn (deep belief networks), dnn (deep neural networks), cnn (convolutional neural networks), have gradually begun to be applied in the field of PD pattern recognition [19][20][21]. In 2016, Mingzhe Rong used convolutional neural network for PD pattern recognition.…”

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confidence: 99%

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Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm

Zhou

Pei

et al. 2019

Energies

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In view of the fact that the statistical feature quantity of traditional partial discharge (PD) pattern recognition relies on expert experience and lacks certain generalization, this paper develops PD pattern recognition based on the convolutional neural network (cnn) and long-term short-term memory network (lstm). Firstly, we constructed the cnn-lstm PD pattern recognition model, which combines the advantages of cnn in mining local spatial information of the PD spectrum and the advantages of lstm in mining the PD spectrum time series feature information. Then, the transformer PD UHF (Ultra High Frequency) experiment was carried out. The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.In recent years, with the rapid development of recognition technologies in the research fields such as images, texts, and videos, classification algorithms based on deep learning, for example, dbn (deep belief networks), dnn (deep neural networks), cnn (convolutional neural networks), have gradually begun to be applied in the field of PD pattern recognition [19][20][21]. In 2016, Mingzhe Rong used convolutional neural network for PD pattern recognition. The simulation results show that the recognition accuracy of cnn is better than the Hilbert-Huang transform and wavelet entropy-characterized support vector machine (SVM) [22]. In 2017, Zheng used cnn to identify the pattern of transformer PD. The experimental results show that the recognition rate of cnn is better than the traditional identification method [18]. In 2018, Jiang and Sheng applied cnn to the pattern recognition of GIS PD. The PRPS (phase resolved pulse sequence) map and the whole-period time-domain waveform map were used as input. The research shows that the recognition accuracy of cnn is better than SVM and BP (back propagation) neural network [23,24]. In the same year, Peng used cnn for pattern recognition of cable PD. The results showed that the overall recognition accuracy of cnn increased by 3.71% and 4.06%, respectively, compared with SVM and BP neural network [25]. Nguyen used lstm to identify the partial discharge pattern of GIS (Gis Insulation Switchgear). The experimental results show that the recognition accuracy of lstm is better than SVM [26]. Adam applied lstm to the identification of different PD defect types in insulating oil. The time domain single-pulse electrical signal was used as input. Experimental results show that the recognition accuracy of lstm is slightly lower than the random forest method. However, since the lstm method has the advantage of not requiring human statistical recognition features, it is still recommended by the a...

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“…The approach presented in [15] classified six different PD defects constructed in oil by using a deep neural network, which processed phase-resolved PD (PRPD) pattern as images. In [16], the authors used a CNN to classify PD sources in a gas insulated system (GIS). That paper showed that the deep network improved on state-of-art algorithms.…”

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confidence: 99%

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

et al. 2020

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The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices.

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GIS partial discharge pattern recognition via deep convolutional neural network under complex data source

Cited by 132 publications

References 25 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

Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

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