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

Xiu Zhou, Xutao Wu, Pei Ding, Xiuguang Li, Ninghui He, Guozhi Zhang and Xiaoxing Zhang
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Xiu Zhou: Ningxia Power Research Institute of State Grid, Yinchuan 750000, China
Xutao Wu: Ningxia Power Research Institute of State Grid, Yinchuan 750000, China
Pei Ding: Ningxia Power Research Institute of State Grid, Yinchuan 750000, China
Xiuguang Li: Ningxia Power Research Institute of State Grid, Yinchuan 750000, China
Ninghui He: Ningxia Power Research Institute of State Grid, Yinchuan 750000, China
Guozhi Zhang: Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
Xiaoxing Zhang: Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China

Energies, 2019, vol. 13, issue 1, 1-13

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.

Keywords: electrical power; transformer; insulation; partial discharge; pattern recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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