情态动词
变压器
溶解气体分析
计算机科学
断层(地质)
局部放电
电力系统
时间序列
模态分析
数据挖掘
工程类
可靠性工程
功率(物理)
机器学习
电压
电气工程
化学
物理
量子力学
地震学
地质学
高分子化学
变压器油
结构工程
有限元法
标识
DOI:10.1016/j.ijepes.2022.108567
摘要
Fault diagnosis is important to the timely repair of the power transformer. However, machine learning has not been exploited effectively for fault diagnosis due to the limitation of multi-modal heterogeneity of data and the ratio of missing samples. To solve this problem, a novel multi-modal information analysis method is presented to effective and speedy evaluate power transformer fault with time sequences and multi-modal data. The proposed method consists of a Selective Kernel Network, a bidirectional gated recurrent unit, and a cross attention mechanism. The proposed approach is verified by datasets of dissolved gas and infrared image modes which come from real power transformers and the historical data. The results show the advantage and efficiency of the proposed method for its higher diagnostic accuracy and shorter diagnostic time than those of the comparison approaches.
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