Frequency Feature Learning from Vibration Information of GIS for Mechanical Fault Detection.

Yang Yuan,Xiaowu Luo,Suliang Ma,Weixin Li,Jianwen Wu,Bowen Jia

doi:10.3390/s19081949

Yang Yuan, Xiaowu Luo + Show 4 more

Open Access

https://doi.org/10.3390/s19081949

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Journal: Sensors (Basel, Switzerland)	Publication Date: Apr 25, 2019
Citations: 19	License type: CC BY 4.0

Affiliation: Beihang University

Abstract

The reliability of gas insulated switchgear (GIS) is very important for the safe operation of power systems. However, the research on potential faults of GIS is mainly focused on partial discharge, and the research on the intelligent detection technology of the mechanical state of GIS is very scarce. Based on the abnormal vibration signals generated by a GIS fault, a fault diagnosis method consisting of a frequency feature extraction method based on coherent function (CF) and a multi-layer classifier was developed in this paper. First, the Fourier transform was used to analyze the differences and consistency in the frequency spectrum of signals. Secondly, the frequency domain commonalities of the vibration signals were extracted by using CF, and the vibration characteristics were screened twice by using the correlation threshold and frequency threshold to further select the vibration features for diagnosis. Then, a multi-layer classifier composed of two one-class support vector machines (OCSVMs) and one support vector machine (SVM) was designed to classify the faults of GIS. Finally, the feasibility of the feature extraction method was verified by experiments, and compared with other classification methods, the stability and reliability of the proposed classifier were verified, which indicates that the fault diagnosis method promotes the development of an intelligent detection technology of the mechanical state in GIS.

Highlights

As a piece of control and protection equipment in power system [1,2], gas insulated switchgear (GIS) plays a significant role in high-voltage power grids
The ultra high frequency (UHF) method was used to analyze the characteristics of partial discharge, and short-time Fourier transform (STFT) [11] was used to describe the time-frequency characteristics [12,13]
After the parameters are determined, 10 experiments were conducted to test the stability of the diagnosis model, and the results are compared with the methods of Softmax, support vector machine (SVM), SVM, back propagation neural networks (BPNN)

Summary

Introduction

As a piece of control and protection equipment in power system [1,2], gas insulated switchgear (GIS) plays a significant role in high-voltage power grids. Discovering potential defects and hidden danger in the process of operation of GIS equipment in time can ensure the reliability and security of power grid operations. Aiming at the condition monitoring and diagnosis of gas insulated structures, a real-time measurement system combining signal acquisition, mode generation, feature extraction and defect recognition was proposed [10]. The ultra high frequency (UHF) method was used to analyze the characteristics of partial discharge, and short-time Fourier transform (STFT) [11] was used to describe the time-frequency characteristics [12,13]. Combined weight function classification tools and K-means clustering, and pulse parameters in both time and frequency domains were used to effectively identify noise signals and discharge pulses [14]

Results

Discussion

Conclusion