Health condition assessment of transformers based on cross message passing graph neural networks

Abstract

A method is proposed to assess the health condition of transformers based on cross message passing graph neural networks (CMPGNNs) in this paper. In order to improve the accuracy of transformer health condition assessment, multiple indicators and their strong correlation are taken into account. The evaluation indicators are divided into four comprehensive state categories, and each category has several indicators. First, the correlation between indicators of a state category is extracted by the health index method and the importance of criteria is analyzed through the inter-criteria correlation (CRITIC) method, and the health index of comprehensive indicators is obtained. Then, a diagram of comprehensive indicators is constructed, and the correlation between indicators of different state categories is extracted. Finally, CMPGNN is constructed to achieve the health assessment. The experimental results show that the proposed method can improve the accuracy of transformer health condition assessment.