Non-dispersion infrared SF₆ gas sensor with air pressure compensation based on RBF neural network

Non-dispersive infrared SF₆ gas sensor has many advantages, such as wide measurement range, high sensitivity, strong anti-interference ability and so on. It has been widely used in power system. However, in the actual detection process, the change of the ambient pressure has a great impact on the detection accuracy of the gas sensor.Aiming at this, an air pressure compensation model of gas sensor was established by using radial basis function (RBF) neural network, and its measurement error is compensated by the good generalization and non-linear mapping ability. The experimental results show that when the designed air pressure compensation model of gas sensor is in the gas concentration of 3 260 mg/m³~9 781 mg/m³, and the air pressure is in the range of 100 kPa~120 kPa, the maximum measurement error is reduced from ±646 mg/m³ to ±52 mg/m³, the measurement accuracy is ±0.53%FS. Compared with the empirical formula method and the constant air pressure compensation, this method has higher measurement accuracy and stability, and reduces the volume and cost of the sensor.