Abstract
In order to realize on-line real-time detection of HF gas in gas chamber of Gas Insulated Switchgear (GIS) of high-voltage composite electric appliances, and to perfect defect diagnosis of high-voltage electrical equipment based on gas chemical composition analysis, HF gas was detected online by tunable diode laser absorption spectroscopy (TDLAS) technology of 1321nm DFB laser. Simulation results show that changes in temperature and pressure will affect the broadened center wavelength and intensity of the HF gas absorption line. In order to make up for the detection error caused by the change of the second harmonic signal with the ambient temperature and pressure, HF/SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> standard gas with volume fraction of 50×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> and BP neural network model algorithm were used to compensate the temperature and pressure. Calibration experiments show that the amplitude of the second harmonic signal is proportional to the standard concentration of HF gas, the fitting coefficient R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is 0.9995, and the detection limit is 5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> . The results show that the BP neural network temperature and pressure compensation algorithm can effectively improve the measurement accuracy, anti-interference ability and repeatability of the detection system.
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