Research on transformer fault diagnosis based on active learning with imbalanced data of dissolved gas in oil

Author:

Tang Pengfei1ORCID,Zhang Zhonghao1,Tong Jie1ORCID,Ma Zhenyuan1ORCID,Long Tianhang1ORCID,Huang Can1,Qi Zihao1

Affiliation:

1. China Electric Power Research Institute , Haidian District, Beijing 100192, China

Abstract

The power transformer is the core equipment of the power system, a sudden failure of which will seriously endanger the safety of the power system. In recent years, artificial intelligence techniques have been applied to the dissolved gas analysis evaluation of power transformers to improve the accuracy and efficiency of power transformer fault diagnosis. However, most of the artificial intelligence techniques are data-driven algorithms whose performance decreases when the data are limited or significantly imbalanced. In this paper, we propose an active learning framework for power transformer dissolved gas analysis, in which the model can be dynamically trained based on the characteristics of the data and the training process. In addition, this paper also improves the original active learning spatial search strategy and uses the product of sample feature differences instead of the original sum of differences as a measure of sample difference. Compared to passive learning algorithms, the novel approach could significantly reduce the data labeling effort while improving prediction accuracy.

Funder

The Scienece and Technology Project of SGCC

Publisher

AIP Publishing

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