Under the coupling effect of equipment noise and external environmental interference, the real partial discharge signal is masked or distorted, which increases the difficulty of feature extraction. Therefore, a partial discharge signal feature extraction algorithm based on feature pattern decomposition is proposed. Using wavelet transform to analyze the main edges of the signal, calculate the correlation coefficients between each scale and adjacent scales, and eliminate the noise of partial discharge signals. Using the variational mode decomposition method to decompose the discharge signal, calculating the multi-scale entropy of the intrinsic mode components,and further filtering out interference; Combining the kernel principal component analysis method to reduce the feature parameters of the input signal, calculate the energy, modulus, and absolute mean of the frequency band projection sequence to complete feature extraction. Experimental results have shown that the proposed algorithm effectively extracts partial discharge features, provides detailed fault information, and ensures stable operation.

feature pattern decomposition, partial discharge signal, wavelet transform, feature extraction, correlation coefficient