基于STFT-ResBIGRUNet的高压变压器的多式融合故障诊断
Jindou Yuan1, Chang Liu2, Bowen Zheng2
1State Key Laboratory of Power Grid Safety (China Electric Power Research Institute Co., Ltd.), Beijing, 100192, China. yuanjindou@126.com.
Scientific reports
|November 29, 2025
概括
一种新方法使用多源信息融合用于高压变压器故障诊断. 在复杂的电力系统中,STFT-ResBiGRUNet模型提高了准确性和可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 在工程领域的人工智能.
背景情况:
- 高压变压器对于电力系统的稳定性和安全性至关重要.
- 复杂的操作环境和不同的负载挑战了变压器的可靠性.
- 单信号故障诊断对于现代电力系统来说是不够的.
研究的目的:
- 开发用于高压变压器的智能故障诊断方法.
- 通过使用多源信息融合,提高诊断准确性和全面性.
- 解决传统故障诊断技术的局限性.
主要方法:
- 提出了一种多来源信息融合方法:STFT-ResBiGRUNet.
- 使用了短时间弗里埃变换 (STFT),残余网络 (ResNet18) 和双向门式反复单位 (BiGRU).
- 集成高效通道注意力 (ECA) 机制用于使用电气和环境数据增强特征表示.
主要成果:
- 该STFT-ResBiGRUNet模型在故障诊断中实现了高精度和稳定性.
- 与最先进的分类模型相比,表现出优越的性能,特别是在杂的环境中.
- 成功地将本地特征提取与全球时间依赖模型相结合.
结论:
- 多源信息融合显著提高了高压变压器故障诊断.
- 拟议的STFT-ResBiGRUNet模型为复杂的电力系统提供了智能和准确的解决方案.
- 这种方法提供了一种可靠的方法来确保电力基础设施的稳定性和安全性.
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