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

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.