Insulation Fault Diagnosis of Disconnecting Switches Based on Wavelet Packet Transform and PCA-IPSO-SVM of Electric Fields

Tongqiang Yi,Hongye Zhang,Xu Kong,Yanzhao Xie

doi:10.1109/access.2020.3026932

Abstract

Previous studies have shown that switching operations of gas insulated substations (GIS) can generate transient radiation fields outside the enclosure, namely switching transient electric fields (STEF). The waveform features of STEF can reflect the functioning performance of the switch. To monitor online the working states of disconnecting switches (DS), in this paper, we built an experimental platform to simulate their typical faulty types. Then, under different faulty status, a non-invasive three-dimensional (3D) electric field measurement system was applied to obtain STEF produced by DS. It is difficult for conventional methods to establish an accurate fault-diagnosis model, so we presented a novel method to identify the condition of DS. This innovative approach is based on feature extraction and machine learning and combined signal analysis to classify different defect types of DS. Measured STEF signals were analyzed by the wavelet packet transform(WPT) method in the time-frequency domain, which was transformed to the multi-dimensional feature matrix. The principal component analysis (PCA) algorithm was employed to reduce the dimensionality of the obtained feature matrix, which was also compared to other feature extraction algorithms. In addition, a support vector machine (SVM) with an improved particle swarm optimization (IPSO) algorithm was designed to achieve a PCA-IPSO-SVM model which can be used for signal recognition. The proposed IPSO technique can improve the convergence performance of the PSO through the dynamic adjustment of inertia weight and learning factors. Results show that the proposed fault diagnosis method based on WPT and PCA-IPSO-SVM can effectively identify the insulation faulty signals in STEF.

Highlights

Disconnecting switches (DS) are key components of gas insulated substations (GIS), which serve in isolating high voltage and ensuring the safety of high-voltage electrical equipment during maintenance
Hao et al [17] compared the differences in wavelet packet energy of the electric field in normal state and defect state of the circuit breaker (CB), and proposed a fault diagnosis method based on wavelet packet energy of switching transient electric field (STEF)
radial basis kernel function (RBF) is selected as the kernel function of support vector machine (SVM), and improved particle swarm optimization (IPSO) is used to optimize the penalty coefficient C and RBF kernel parameter σ

Summary

INTRODUCTION

Disconnecting switches (DS) are key components of gas insulated substations (GIS), which serve in isolating high voltage and ensuring the safety of high-voltage electrical equipment during maintenance. Hao et al [17] compared the differences in wavelet packet energy of the electric field in normal state and defect state of the CB, and proposed a fault diagnosis method based on wavelet packet energy of STEF. In order to extract the feature parameters from a large amount of time-frequency domain data by WPT, it is necessary to highlight the characteristic ability of the STEF signal. An intelligent defect diagnosis approach based on the measurement and analysis of STEF is proposed for DS, which combines WPT, PCA, IPSO, and SVM. PCA is to study the correlation between variables and replace the original variables with a new set of less and unrelated variables, to retain as much information as possible It is a data analysis method based on second-order statistics, which has a better ability for reducing dimension and noise filtration [27]. When the cumulative contribution rate of the k principal components is greater than 90%, U contains most of the information of the original data

OPTIMIZ7E THE PARAMETERS OF SVM BY IMPROVED PSO

FORECAST RESULTS AND ANALYSIS

CONCLUSION