基于GWO-BP神经网络补偿的SF6红外气体传感器

SF6 infrared gas sensor based on GWO-BP neural network

为实现电力系统中SF 6气体的有效监测与控制,本文基于非色散红外原理(NDIR),设计了一种SF 6气体传感器。但是,在实际的测量中,环境的温度与气压差异性容易影响SF 6气体浓度检测装置的检测精度,因此需要采取适当的方法消除环境引起的测量误差。本文采用灰狼智能优化算法—误差反向传播(GWO-BP)神经网络对环境温度与气压变化引起的测量误差进行了补偿,并与其他补偿方法作了比较。分析得出:进行补偿后的浓度数据在0~2000 ppm范围内误差为±15 ppm,满量程误差为0.75%FS,有效提升了传感器的测量精度与稳定性。相较于电路补偿法,该方法有更高的测量精度,并且降低了传感器的体积和成本。

In order to effectively monitor and control the concentration of SF 6 gas in power system,a SF 6 gas sensor is designed based on the principle of non-dispersive infrared(NDIR).However,in the actual detection process,the difference between the ambient temperature and pressure has a great impact on the detection accuracy of the SF 6 gas sensor,so it is necessary to adopt appropriate methods to eliminate the measurement error caused by the environment.In this paper,GWO-BP neural network was used for real-time compensation for the measuring deviations caused by changing temperature and pressure,and then compared with other compensation approaches.Analysis shows that the measurement accuracy of concentration data after compensation is less than±15 ppm in a range of 0 to 2000 ppm,with the full range accuracy up to 0.75%.It effectively improves the sensor measurement accuracy and reduces the volume and cost of the sensor meanwhile.