Quantitative Recognizing Dissolved Hydrocarbons with Genetic Algorithm-Support Vector Regression - Qu Zhou; Weigen Chen; Xiaoping Su; Shudi Peng (Institute of Advanced Engineering and Science, 2013)
Online monitoring of dissolved fault characteristic hydrocarbon gases, such as methane, ethane, ethylene and acetylene in power transformer oil has significant meaning for condition assessment of transformer. Recently, semiconductor tin oxide based gas sensor array has been widely applied in online monitoring apparatus, while cross sensitivity of the gas sensor array is inevitable due to same compositions and similar structures among the four hydrocarbon gases. Based on support vector regression (SVR) with genetic algorithm (GA), a new pattern recognition method was proposed to reduce the cross sensitivity of the gas sensor array and further quantitatively recognize the concentration of dissolved hydrocarbon gases. The experimental data from a certain online monitoring device in China is used to illustrate the performance of the proposed GA-SVR model. Experimental results indicate that the GA-SVR method can effectively decrease the cross sensitivity and the regressed data is much more closed to the real values. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.2776