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. INDEX TERMS Gas insulated substation (GIS); disconnecting switch (DS); switching transient electric field (STEF); wavelet packet transform(WPT); principal component analysis (PCA); support vector machine (SV M); improved particle swarm optimization (IPSO)

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In order to monitor the state of Gas Insulated Substation (GIS), this paper established a fault simulation experimental platform for Disconnecting Switch (DS), and collected data through a 3D radiation electric field (E-field) measurement system. For extract the feature parameters, four-layer wavelet packet decomposition was performed on the data to obtain normalized energy, and the dimension of the eigenvector was reduced by principal component analysis (PCA) algorithm. Then, the model was trained with the hybrid kernel support vector machine (HSVM) algorithm, and the parameters was optimized with the particle swarm optimization(PSO) algorithm. The result shows that compared with the traditional SVM model, the method proposed in this paper improves the diagnosis accuracy of DS defect signals.