A Machine-Learning-Algorithm Enhanced Multi-Functional Gas Sensor for Self-Humidity Compensation and Partial Discharge Detection.
Journal:
ACS sensors
Published Date:
Aug 13, 2025
Abstract
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.
