Multi-Dimensional Diagnosis of Transformer Fault Sample and Credibility Analysis
Li Dianyang1,2, Zhang Yujie1,3, Feng Jian1, Wang Shanyuan1
1. Information Science and Engineering College Northeastern University Shenyang 110819 China; 2. State Grid Liaoning Electric Power Limited Company Shenyang 110006 China; 3. State Grid Xinxiang Electric Power Supply Company Xinxiang 453005 China
Abstract：Power transformer is the core equipment of power system, and its safe and stable operation is of great significance to power system. Online fault diagnosis of power transformers is an important method to realize real-time status analysis of power transformers. At present, the selection of features subset for transformer fault diagnosis mainly adopts heuristic-based method, which simplifies the selection process compared to traversal algorithms, but it still consumes a lot of computing power. Moreover, the research of hybrid fault diagnosis motheds for power transformer focuses on the improvement of the diagnosis effect in all samples, and does not pay attention to the credibility analysis of the diagnosis results in single sample. In order to solve this problem, this paper proposed a transformer state analysis method that combines autonomous discretization and optimization of data distribution signs, and single-event multi-model fusion analysis. Proved by examples, this method can effectively analyze the data distribution of each feature, perform feature optimization, and can get the operating status and reliability of the transformer from the perspective of a single event.
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2022, Vol. 37 
