Study on transformer fault diagnosisbased on improved deep residual shrinkage network and optimized residual variational autoencoder

Abstract

The transformer as the core equipment in the power system, its fault diagnosis has a vital role in ensuring the safe and stable operation of the power grid. However, traditional transformer fault diagnosis methods often rely on manual experience or simple models, which are difficult to meet the demand for efficient and accurate diagnosis when faced with complex and evolving fault patterns. In this study, a new method for transformer fault diagnosis based on improved deep residual shrinkage network (DRSN) and optimized residual variational autoencoders (ORVAE) is proposed. Firstly, this study improves the DRSN to enhance its feature extraction capability. By designing a specific shrinkage mechanism, the improved DRSN can reduce the information loss in the face of complex data, greatly improve the extraction ability of the key features of the transformer operating state, and thus improve the accuracy of fault recognition. Secondly, in view of the difficulty and high cost of transformer fault sample data collection, this study introduces a residual connection structure based on the traditional variational autoencoder (VAE), and constructs the ORVAE method to effectively address the challenge of insufficient data. The results show that the fault recognition rate of the proposed method on the real transformer fault dataset reaches 97.14 %, which is better than the traditional method, showing excellent diagnostic performance and strong practical application potential. Compared with the existing technologies, this method not only improves the accuracy of transformer fault diagnosis, but also provides new ideas and technical support for the intelligent development of power system. This study offers an innovative solution for the field of fault diagnosis of power equipment, and providing a strong technical guarantee for fault prediction and maintenance in future smart grids.