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Power transformer is pivotal equipment in a power system, which is responsible for energy transmission and transformation, and its operating condition is related to the safe operation of the power system. In the 21st century, computer science has entered a stage of rapid development, advanced network structures and algorithms have been applied to the field of artificial intelligence, and pattern recognition theory and technology have also made great progress. In the past, the identification of partial discharge type mainly relied on the experience of operation and maintenance personnel, and manual analysis and judgment were made based on partial discharge mapping, which was not very accurate. The application of the computer pattern recognition method in the field of partial discharge type identification has changed the status quo of manual identification, and this method has substantially improved the accuracy and efficiency of identification. Pattern recognition using computer technology has been applied to the field of partial discharge analysis. Compared with manual recognition, its recognition results are accurate, recognition speed is fast, and it has great potential for development. This paper proposes an artificial neural network-based model for transformer partial discharge pattern recognition, which combines the advantages of artificial neural networks with accurate extraction of local spatial higher-order features and provides a new solution for transformer partial discharge pattern recognition. Extended experiments show that the method proposed in this paper achieves leading performance and has practical application value.

Research on Pattern Recognition Method of Transformer Partial Discharge Based on Artificial Neural Network