Analysis Of The Gas-sensitive Property Of SnO2Semiconductor Sensors And Its Application In The DGA

SnO2semiconductor sensors have excellent comprehensive performance among Metal-oxide-semiconductor (MOS) gas sensors with low prices, simple structure, fast response and a service life. They are also very sensitive to flammable gases and volatile organic gases. So it’s widely used in fire prevention, environmental pollution detection, etc. Tin oxide semiconductor sensor has excellent comprehensive performance among MOS sensors. After studying basic physical properties of SnO2and the work principle of the interaction between gas molecular on the surface, sensitivity characteristics of the sensor were analyzed. A lot of testing was done including the sensitivity of the sensor, the selectivity, the response and recovery time, the temperature characteristic, the humidity characteristic and the drift. Analysis of the tests results gave solutions for the drift and the.The Sn content in the sensor and the effects of additives on the detection sensitivity of the sensor were studied. And a sensor which was highly sensitive to six kinds of gases in the transformer oil was selected. Through the optimization reorganization of chromatographic techniques and sensor and separating the mixed gases using the chromatographic columns, the problem of selectivity of the sensor was completely solved. The real-time requirements were met while a good gas separation characteristic was achieved. The goal of achieving a detector which could accurately monitor a variety of feature gases was realized. Due to the sensitive nature, the sensor would drift in terms of environmental factors which could lead to measurement error and poor compatibility. So according to the characteristics that the conductivity of the sensor changed with the concentration change of the gas, a piecewise power function model between the output signal and the gas concentration was constructed through a deep analysis of the gas response curve. The test results showed that the algorithm improved the accuracy of quantitative analysis with strong environmental applicability and improved the reliability and precision of features gas detection.