Multi-Scale Analysis and Pattern Recognition of Ultrasonic Signals of PD in a Liquid/Solid Composite of an Oil-Filled Terminal

Yulong Wang, Xiaohong Zhang, Yancheng Li, Lili Li, Junguo Gao and Ning Guo
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Yulong Wang: Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
Xiaohong Zhang: Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
Yancheng Li: State Grid Corporation of Yantai, Yantai 264001, China
Lili Li: Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
Junguo Gao: Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
Ning Guo: Key Laboratory of Engineering Dielectrics and its Application, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China

Energies, 2020, vol. 13, issue 2, 1-21

Abstract: In order to analyze the partial discharge (PD) characteristics of a liquid/solid composite medium in an oil-filled submarine cable terminal; we have designed five discharge models including needle-plate, plate-to-plate air gap, surface, slide-flash and suspension potential. At the same time, the ultrasonic signals of PD have been extracted through the typical fault model research platform of oil-filled submarine cable equipment. First, we use SureShrink threshold wavelet denoising to suppress the ultrasonic signal noise. Secondly, through the multi-scale analysis of the signal, the energy distribution maps of five different types of PD are obtained; the analysis found that needle-plate discharge, suspension discharge, and slide-flash discharge have better resolution; and plate-to-plate air gap discharge and creeping discharge have similar characteristics and are not easy to distinguish. Finally, we designed six characteristic parameters of the ultrasound signal, and screened three feature quantities by a back propagation (BP) neural network to distinguish between plate-to-plate air gap discharge and surface discharge. In summary, the method of combining multi-scale analysis and neural networks is used to distinguish the five discharge types by extracting the characteristic values of the characteristic signals.

Keywords: high voltage oil-filled cable terminal; multiscale analysis; BP neural network; PD 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: 2020
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