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

Abstract

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...

Research on Pattern Recognition Method of Transformer Partial Discharge Based on Artificial Neural Network

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Deep Learning in High Voltage Engineering: A Literature Review

“…Four typical transformer insulation defects were simulated in [16] that include metal protrusion, oil paper void, surface discharge, and floating potential defects. The authors developed a CNN-LSTM based model where the input is the PRPD data.…”

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