改进了一维残余网络高压直流诊断,用于高精度故障识别
Rui Li1, Xiaopeng Zhang1, Wei Hao1
1State Grid Shanxi Electric Power Research Institute, Taiyuan, China.
PloS one
|January 20, 2026
概括
本研究介绍了一种注意力增强的1D-ResNet用于高压直流 (HVDC) 故障诊断. 改进的模型达到99.15%的准确性,显著提高了动力系统的可靠性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力系统 电力系统
背景情况:
- 高压直流 (HVDC) 传输需要强大的故障诊断,以保持电网稳定.
- 传统的卷积神经网络 (CNN) 面临的准确性和训练挑战越来越深入.
研究的目的:
- 开发HVDC系统的先进故障诊断方法.
- 与现有技术相比,提高准确性,稳定性和融合速度.
主要方法:
- 提出了一个改进的单维残余神经网络 (1D-ResNet),其中包含了注意力机制.
- 一个模拟的HVDC平台生成了正常和六个故障条件的数据.
- 该网络被训练来分类这些条件.
主要成果:
- 提高注意力的1D-ResNet实现了99.15%的平均诊断准确率.
- 这比传统的CNN方法有12.89%的改进.
- 该模型显示了明显较低的损失值,表明学习效率和稳定性得到了提高.
结论:
- 提议的注意力增强的剩余框架对于精确的高频直流故障诊断是非常有效的.
- 这种方法为电力系统监控的准确性和学习效率提供了显著的改进.
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