Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.

Partial discharge (PD) detection is essential in assessing the insulation state of electrical equipment. However, PD signals are often overwhelmed by interference, resulting in inaccurate detection results. Aiming at this problem, this study proposes a PD detection method based on singular value decomposition (SVD) and improved spectral subtraction. First, the test signal is constructed as a Hankel matrix, which is used as a trajectory matrix for the SVD. Next, the singular value mutation point in the feature matrix is set as the threshold for removing the narrowband interference (NBI), and a signal containing only white noise is obtained. Finally, the improved spectral subtraction is used to remove white noise and improve the signal-to-noise ratio (SNR). The method proposed herein, along with the variational mode decomposition, the empirical mode decomposition, and the improved threshold wavelet method, are applied to the processing of PD signals. Also, the SNR value, waveform similarity coefficient, and mean square error of the denoizing signal of the four algorithms were calculated, considering the noise suppression and feature preservation abilities. The simulation and measurement results show that the SVDspectral subtraction method has a strong suppression effect on narrow-band interference and white noise. Compared with other algorithms, this method can significantly improve the execution efficiency and has great application prospects.

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.

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