Analysis and Research of High Voltage Cable Head Insulation Discharge Diagnosis Based on Various Detection Methods

Authors

  • Yang Zhao State Grid Beijing Powercable Company, Beijing 100022, China
  • Qing Liu State Grid Beijing Powercable Company, Beijing 100022, China
  • Tong Shang State Grid Beijing Powercable Company, Beijing 100022, China
  • Yingqiang Shang State Grid Beijing Powercable Company, Beijing 100022, China
  • Rong Xia China Electric Power Research Institute Limited Wuhan Branch, Wuhan 430079, China
  • Shuai Shao Center of Jinan Power Supply Company of State Grid Shandong Electric Power Company, Jinan 250012, China

Abstract

Because there are many and complex types of high-voltage cable head insulation discharge, the accuracy of the discharge diagnosis decreases and the diagnosis time increases. Therefore, a new method for diagnosing high-voltage cable head insulation discharge based on multiple detection methods is proposed and tested. The proposed method involves using the fiber Bragg grating to collect the insulation discharge acoustic signal of high voltage cable head, and using subspace reconstruction, frequency slicing wavelet and fast independent component analysis to suppress interferences due to periodic narrow-band, white noise, and random impulse. The M-ary support vector machine is used to classify the insulation discharge signals of a
high-voltage cable head, and achieve the discharge diagnosis. The experimental results show that for the four types of typical high-voltage cable head insulation discharges (needle plate discharge, internall discharge, suspension discharge and surface discharge), the proposed method achieves a high level of diagnostic accuracy and the diagnosis time is short. Hence, the proposed method can achieve rapid and accurate diagnosis of high-voltage cable head insulation discharge.

Keywords: high-voltage cable head; insulation discharge diagnosis; fiber Bragg grating; subspace reconstruction; frequency slice wavelet; fast independent component analysis; M-ary support vector machine

Cite As

Y. Zhao, Q. Liu, T. Shang, Y. Shang, R. Xia, S. Shao, "Analysis and Research of High Voltage Cable
Head Insulation Discharge Diagnosis Based on Various Detection Methods", Engineering Intelligent
Systems, vol. 32 no. 2, pp. 105-115, 2024.



 

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Published

2024-03-01

Issue

Vol. 32 No. 2 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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