Han Yutong, Zhuang Haozhe, Yin Ziyang, Long Zhengqing, Li Yue, Yao Yu, Zheng Qibin, Zhu Zhigang
School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China.
ACS Sens. 2025 Aug 22;10(8):5882-5891. doi: 10.1021/acssensors.5c01214. Epub 2025 Aug 13.
Gas-Insulated switchgear (GIS) is prone to partial discharges (PDs) in high electric field environments, and the concentration of generated NO is an essential indicator for determining the PD types and severity of faults. Notably, environmental humidity greatly influences the insulation performance of gas-insulated switchgear and the signals of NO gas sensors. Thus, the simultaneous detection of humidity and NO and the decoupling of signals has practical importance. Herein, a groundbreaking sensor is developed to achieve self-calibrated sensing of humidity and NO gas, which is realized by a multifunctional WS/ZnO sensitive material with an innovative self-humidity compensation algorithm of DF-MT1DCL. This synergistic system delivers dynamic, real-time humidity adaptive calibration and also enables precise recognition of partial discharge types. The sensor exhibited simultaneous response and a wide detection range (100 ppb-10 ppm of NO, 10.8-94.3% RH) exposed to NO and humidity at room temperature. As a result, simultaneous monitoring and decoupling of signals can be realized. Further, a multitask deep learning model DF-MT1DCL combined 1D-CNN with LSTM was proposed to complete the humidity adaptive calibration based on a single WS/ZnO sensor, which realizes the simultaneous prediction of humidity and NO concentration, with values of 99.1% and 93.5% respectively. The WS/ZnO sensor with excellent humidity and NO sensing performance and the DF-MT1DCL algorithm assistance was applied to partial discharge monitoring in a simulated gas-insulated switchgear, and high-precision classification of partial discharge types was achieved with 100% classification accuracy. Therefore, the constructed WS/ZnO multifunctional sensor combined with the DF-MT1DCL algorithms improves the resistance to humidity interference of NO detection and also accurately recognizes the partial discharge type, which provides a new perspective for the intelligent sensing technology for health monitoring of electric power equipment.
气体绝缘开关设备（GIS）在高电场环境中容易发生局部放电（PD），产生的一氧化氮（NO）浓度是确定局部放电类型和故障严重程度的重要指标。值得注意的是，环境湿度对气体绝缘开关设备的绝缘性能和NO气体传感器的信号有很大影响。因此，同时检测湿度和NO并进行信号解耦具有实际意义。在此，开发了一种开创性的传感器，以实现对湿度和NO气体的自校准传感，这是通过具有创新的DF-MT1DCL自湿度补偿算法的多功能WS/ZnO敏感材料实现的。这种协同系统提供动态、实时的湿度自适应校准，还能精确识别局部放电类型。该传感器在室温下暴露于NO和湿度时表现出同时响应和宽检测范围（NO为100 ppb - 10 ppm，相对湿度为10.8 - 94.3%）。结果，可以实现信号的同时监测和解耦。此外，提出了一种将一维卷积神经网络（1D-CNN）与长短期记忆网络（LSTM）相结合的多任务深度学习模型DF-MT1DCL，以基于单个WS/ZnO传感器完成湿度自适应校准，实现湿度和NO浓度的同时预测，预测值分别为99.1%和93.5%。具有优异湿度和NO传感性能的WS/ZnO传感器以及DF-MT1DCL算法辅助被应用于模拟气体绝缘开关设备中的局部放电监测，并以100%的分类准确率实现了局部放电类型的高精度分类。因此，构建的WS/ZnO多功能传感器与DF-MT1DCL算法相结合，提高了NO检测对湿度干扰的抗性，还能准确识别局部放电类型，为电力设备健康监测的智能传感技术提供了新的视角。
