精确识别基于S转换和二维卷积网络算法的高速列车电缆终端部分排放.
Yunlong Xie1, Peng You1,2, Guangning Wu1
1School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了一种使用斯托克威尔变换 (ST) 和2DCNN的新型模型,以准确区分高速列车电缆中的部分放电与冠状干扰. 该方法可达到高达98.75%的准确性,改善了绝缘监控.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 材料科学 材料科学 材料科学
背景情况:
- 电缆终端对于高速列车的能量传输至关重要,但也带来了绝缘方面的挑战.
- 部分放电 (PD) 信号是评估绝缘状态的关键,但经常受到外部冠状干扰的污染.
- 这种干扰显著降低了PD检测和绝缘诊断的准确性.
研究的目的:
- 开发一个先进的信号识别模型,准确地区分部分放电 (PD) 和冠状干扰.
- 提高高速列车电缆终端隔热状态评估的可靠性.
- 克服现有方法的局限性,特别是在长时间序列数据中的干扰截断方面.
主要方法:
- 一个混合信号识别模型,结合了斯托克威尔变换 (ST) 和2D卷积神经网络 (2DCNN).
- 集成基于波纹的降噪技术来预处理信号.
- 利用ST矩阵中的最大能量时刻来纠正时间窗口定位,用于长时间序列分析.
主要成果:
- 拟议的ST和2DCNN模型,与波纹噪声减轻,实现高分类准确度高达98.75%的PD和冠状病毒干扰.
- 基于ST的时间窗口校正有效地防止了冠状病毒干扰的切断,避免了被错误分类为PD.
- 该模型表现出增强的概括能力,在长时间序列信号中有效分离PD和冠状干扰.
结论:
- 综合ST和2DCNN方法为准确的PD和冠状干扰分离提供了强大的解决方案.
- 这种方法显著提高了高速列车电缆系统中绝缘监控的可靠性.
- 该技术解决了现场测试中的关键挑战,提高了诊断精度和系统安全.
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