一个轻量级的深度学习框架用于智能电网中的变压器故障诊断,使用多个尺度的CNN功能.
Omneya Attallah1,2, Rania A Ibrahim3, Nahla E Zakzouk3
1Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, 21937, Egypt. o.attallah@aast.edu.
Scientific reports
|April 25, 2025
概括
本研究介绍了Trans-Light,这是一种深度学习方法,用于使用温度学检测功率变压器故障. 它在识别间转故障和短路严重性方面达到100%的准确性,最大限度地减少停机时间和能源损失.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 智能电网中的电力变压器停机造成了重大经济损失.
- 与温度计一样,非侵入性状态监测对于尽量减少检查期间的能量损失至关重要.
- 深度学习 (DL) 为变压器健康提供高效的智能诊断工具.
研究的目的:
- 提出Trans-Light,一种基于DL的温度计方法,用于检测间转故障和识别功率变压器中短路的严重性.
- 通过从多个CNN层中提取复杂的特征并结合时间频率信息来增强故障诊断.
- 通过特征选择来降低计算负担并提高分类效率.
主要方法:
- 使用卷积神经网络 (CNN) 来从两个层中提取深层特征.
- 应用双树复杂波形变换用于时间频率分析和维度缩小.
- 实施特征选择过程 (例如,chi-square) 以进一步减少特征大小.
- 在不同的噪音条件下测试各种CNN模型,特征选择方法和分类器.
主要成果:
- 在无噪声条件下,ResNet-18 CNN,奇方特征选择和LDA分类器的组合实现了100%的分类准确性.
- 与之前的作品相比,拟议的方法表现出了与最小的特征相比的卓越性能.
- 在各种噪音条件下实现了强大的故障诊断,减少了计算负载和实现复杂性.
结论:
- 变光提供了一种有效和高效的非侵入性方法,用于电力变压器故障诊断.
- 优化的配置显著提高了准确性和稳定性,同时最大限度地降低了计算需求.
- 这种方法有助于减少变压器停机时间,提高智能电网可靠性.
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