溶解气体分析
变压器
计算机科学
人工智能
机器学习
变压器油
可靠性工程
数据挖掘
工程类
电气工程
电压
作者
Peng‐Fei Tang,Zhonghao Zhang,Jian Tong,Zhenyuan Ma,Tianhang Long,Can Huang,Qi Zhou
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI