The pattern recognition of multisource partial discharge in transformers based on parallel feature domain

The traditional diagnosis methods for the multisource partial discharge in transformer have several problems, such as the poor effect of signal separation, the inaccuracy of the extracted signal features and the over fitting of the neural network algorithm used for pattern recognition. In order to solve the above problems, the improved algorithm of stacked auto‐encoder is adopted to extract the adaptive features of partial discharge one dimensional defect signals in time domain and two dimensional defect signal in time frequency domain, and a parallel feature space combining the features in time domain with that the time frequency domain is constructed. For the first time, the transformed L1 norm is utilized as the regular term of the loss function which is solved by the proximally guided stochastic subgradient in the improved auto‐encoder. The trained neural network model can be utilized for pattern recognition of the different multisource partial discharge signals. The results show that the proposed method has a higher recognition rate for the different multisource partial discharge defects than that in the traditional method, and alleviates the problem of over fitting to a certain extent.