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一个机器学习算法增强的多功能气体传感器用于自我湿度补偿和部分放电检测

Yutong Han¹, Haozhe Zhuang¹, Ziyang Yin¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China.

|August 13, 2025

概括

使用WS2/ZnO材料和DF-MT1DCL算法的新传感器可以同时检测气体绝缘开关装置中的湿度和二氧化 (NO2). 该系统准确识别部分放电类型,增强电气设备健康监测.

科学领域:

材料科学材料科学材料科学
传感器技术传感器技术
电气工程电气工程

背景情况:

气体隔热开关设备 (GIS) 在高电场中容易发生部分放电 (PD).
二氧化 (NO2) 度是PD类型和故障严重性的关键指标.
环境湿度对GIS绝缘和NO2传感器信号产生重大影响,需要同时检测和信号脱.

研究的目的:

开发一个自我校准的传感器系统,用于在GIS中同时检测湿度和NO2.
为了分离NO2传感中的湿度干扰,进行准确的PD分析.
实现实时,适应湿度的校准,以实现精确的故障诊断.

主要方法:

开发一种多功能WS2/ZnO敏感材料.
实施了一种新的自我湿度补偿算法,DF-MT1DCL,结合1D-CNN和LSTM.
在室温下测试传感器对NO2和湿度的响应在广泛的检测范围.
传感器算法系统在模拟的GIS环境中用于PD监控.

主要成果:

WS2/ZnO传感器对NO2 (100ppb-10ppm) 和湿度 (10.8-94.3%RH) 显示出同时响应.
DF-MT1DCL算法实现了对湿度 (R2=99.1%) 和NO2 (R2=93.5%) 的高预测精度.
综合系统在模拟的GIS中实现了PD类型的100%分类准确性.

结论:

WS2/ZnO传感器与DF-MT1DCL算法相结合,为NO2检测提供了对湿度干扰的强大抵抗力.
这种协同方法可以实现准确的PD类型识别,推进电力设备健康监测的智能传感.

关键词:

在WS2上,我们可以看到WS2.深度学习是一种深度学习.湿度自我校准算法的算法多功能气体传感器部分放电检测检测部分放电检测

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