An air-insulated power equipment adopts air as the insulating medium and is widely implemented in power systems. When discharge faults occur, the air produces decomposition products represented by NO2. The efficient NO2 sensor enables the identification of electrical equipment faults. However, single-sensor-dependent NO2 detection is vulnerable to interfering gases. Implementing the sensor array could reduce the interference and improve detection efficiency. In the field of NO2 detection, In2O3 sensors have exhibited tremendous advantages. In our work, four composites based on In2O3 are integrated into sensor arrays, which could detect 250 ppb of NO2 and exhibit excellent selectivity when simultaneously exposed to CO. To further reduce the impact of humidity on gas-sensing performance, a convolutional neural network and a long short-term memory model equipped with an attention mechanism are proposed to evaluate NO2 concentration within 1 ppm, and the detection error is 63.69 ppb. In addition, the NO2 concentration estimation platform based on a microgas sensor is established to detect air discharge faults. The average concentration of NO2 generated by 10 consecutive discharge faults at 15 kV is 726.58 ppb, which indicates severe discharge in the switchgear. Our NO2 estimation method has great potential for large-scale deployment in low- and medium-voltage switchgears.
Keywords: CNN–ALSTM; NO2 detection; air discharge; fault diagnosis; gas sensor module; metal oxide semiconductor.