A Machine-Learning-Algorithm Enhanced Multi-Functional Gas Sensor for Self-Humidity Compensation and Partial Discharge Detection

Gas-Insulated switchgear (GIS) is prone to partial discharges (PDs) in high electric field environments, and the concentration of generated NO₂ 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 NO₂ gas sensors. Thus, the simultaneous detection of humidity and NO₂ and the decoupling of signals has practical importance. Herein, a groundbreaking sensor is developed to achieve self-calibrated sensing of humidity and NO₂ gas, which is realized by a multifunctional WS₂/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 NO₂, 10.8-94.3% RH) exposed to NO₂ 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 WS₂/ZnO sensor, which realizes the simultaneous prediction of humidity and NO₂ concentration, with R² values of 99.1% and 93.5% respectively. The WS₂/ZnO sensor with excellent humidity and NO₂ 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 WS₂/ZnO multifunctional sensor combined with the DF-MT1DCL algorithms improves the resistance to humidity interference of NO₂ 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.