![]() 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. 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. This innovative approach is based on feature extraction and machine learning and combined signal analysis to classify different defect types of 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. Then, under different faulty status, a non-invasive three-dimensional (3D) electric field measurement system was applied to obtain STEF produced by DS. To monitor online the working states of disconnecting switches (DS), in this paper, we built an experimental platform to simulate their typical faulty types. The waveform features of STEF can reflect the functioning performance of the switch. Results show that the proposed fault diagnosis method based on WPT and PCA-IPSO-SVM can effectively identify the insulation faulty signals in STEF.Ībstract = "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 proposed IPSO technique can improve the convergence performance of the PSO through the dynamic adjustment of inertia weight and learning factors. 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. Wavelet transform allows us to analyze signals in different frequencies with different resolutions.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). Wider window size will have good frequency resolution but result in bad time resolution. The selection of window size had the following impacts:Ī narrow window size will have good time resolution but result in bad frequency resolution. By fixing a window size, the frequency resolution decreases. The analysis is now dependent on the selection of window size. In STFT, we divide the signal into windows and perform Fourier transform on these windows. This was overcome by introducing the short-time Fourier transform (STFT). It can tell the frequency but not at which time it occurred. But the drawback of Fourier transform is that it only works for stationary signals and most signals in the real world are not stationary. Classifiers can use a frequency spectrum generated by Fourier transform for better classification. Need for the wavelet transformįourier transform converts a signal from the time domain to the frequency domain. Many machine learning applications use the wavelet transform as a preprocessing step. ![]() ![]() It is also a solution to the shortcomings of the Fourier transform. It is also used in data compression, pattern recognition, and more. Wavelet transform is one of the most widely used transforms in signal processing. ![]()
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