Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Wen Si, Simeng Li, Huaishuo Xiao, Qingquan Li, Yalin Shi and Tongqiao Zhang
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Wen Si: Department of Electrical Engineering, Shandong University, Jinan 250061, China
Simeng Li: Department of Electrical Engineering, Shandong University, Jinan 250061, China
Huaishuo Xiao: Department of Electrical Engineering, Shandong University, Jinan 250061, China
Qingquan Li: Department of Electrical Engineering, Shandong University, Jinan 250061, China
Yalin Shi: Jinan Power Supply Company of State Grid Shandong Electric Power Company, #238 Luoyuan Road, Jinan 250012, China
Tongqiao Zhang: Jinan Power Supply Company of State Grid Shandong Electric Power Company, #238 Luoyuan Road, Jinan 250012, China

Energies, 2018, vol. 11, issue 3, 1-19

Abstract: The ultra high voltage direct current (UHVDC) transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD) characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap) under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis.

Keywords: UHVDC transmission system; converter transformer; oil-pressboard insulation; combined AC-DC voltage; defect pattern recognition; partial discharge; random forests (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: 2018
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