Real-Time Monitoring of Air Discharge in a Switchgear by an Intelligent NO<sub>2</sub> Sensor Module
Qiongyuan Wang, Haoyuan Li, Jifeng Chu, Jianbin Pan, Aijun Yang, Song Xiao, Huan Yuan, Mingzhe Rong, Xiaohua Wang
Abstract
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 2 . The efficient NO 2 sensor enables the identification of electrical equipment faults. However, single-sensor-dependent NO 2 detection is vulnerable to interfering gases. Implementing the sensor array could reduce the interference and improve detection efficiency. In the field of NO 2 detection, In 2 O 3 sensors have exhibited tremendous advantages. In our work, four composites based on In 2 O 3 are integrated into sensor arrays, which could detect 250 ppb of NO 2 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 2 concentration within 1 ppm, and the detection error is 63.69 ppb. In addition, the NO 2 concentration estimation platform based on a microgas sensor is established to detect air discharge faults. The average concentration of NO 2 generated by 10 consecutive discharge faults at 15 kV is 726.58 ppb, which indicates severe discharge in the switchgear. Our NO 2 estimation method has great potential for large-scale deployment in low- and medium-voltage switchgears.