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The prediction research of steam reheating temperature in power plants based on LS-SVM

Steam reheating system is emerging as a multivariable system with steam-steam exchanger, the strong coupling and time delay characteristics. The traditional approach for the predictive control in power plant requires modeling based on accurate mathematical model, and some multivariate statistical algorithm cannot avoid falling into the over-fitting, therefore these approaches is not suitable for prediction of the reheating temperature in power plants. In this paper, we used the least squares support vector machine (LS-SVM) regression algorithm to predict the temperature of the steam reheating in the power plant combined with the data set of the steam reheating in a 120MW power plant. Comparing with the existing algorithms, the result shows that the LS-SVM is a robust and reliable tool for prediction in engineering application field.