Real-Time Monitoring of Air Discharge in a Switchgear by an Intelligent NO Sensor Module.

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 NO. The efficient NO sensor enables the identification of electrical equipment faults. However, single-sensor-dependent NO detection is vulnerable to interfering gases. Implementing the sensor array could reduce the interference and improve detection efficiency. In the field of NO detection, InO sensors have exhibited tremendous advantages. In our work, four composites based on InO are integrated into sensor arrays, which could detect 250 ppb of NO 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 NO concentration within 1 ppm, and the detection error is 63.69 ppb. In addition, the NO concentration estimation platform based on a microgas sensor is established to detect air discharge faults. The average concentration of NO generated by 10 consecutive discharge faults at 15 kV is 726.58 ppb, which indicates severe discharge in the switchgear. Our NO estimation method has great potential for large-scale deployment in low- and medium-voltage switchgears.