Fuel Identification Based on the Least Squares Support Vector Machines

Abstract

The identification of the fuel types plays an important role in ensuring the safety and economics of the power plants. In order to obtain the flame signal in the process of combustion, a flame detection system is designed and a laboratorial platform is constructed. This paper extracts the signal parameters—the mean, the peak-peak value, the flicker frequency, and the flicker intensity —and takes them as the characteristic quantities of the flame signal. Based on the least squares support vector machines (LSSVM), an efficient method of identifying the flame types is developed. The result of the identification is more ideal, with the correct identification rate up to 100%. This shows that the method combined the four characteristic quantities with the LSSVM can obtain a good result in the identification of the fuel types.