A Kernel Partial Least Squares Method for Gas Turbine Power Plant Performance Prediction
会议名称：《第24届中国控制与决策会议》
会议日期：2012年
学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学]
基 金：supported by National Nature Science Foundation of China (No.61074074) Project 973 (No.2009CB320601), China the Fundamental Research Funds for the Central Universities (N100604008)
关 键 词：Gas Turbine Power Plant Kernel Partial Least Squares Performance Prediction Off-design Conditions Load Gas Flow
摘 要：The change of the performance of gas turbine power plant may be dramatic under off-design conditions. To describe the off-design performance of gas turbine power plant, good prediction tools are essential. The objective of this paper is to asses the feasibility of the kernel partial least squares (KPLS) technique in performance prediction of gas turbine power plant under off-design conditions. Historical data from the real industrial gas-steam combined cycle of a cogeneration plant unit were used to train KPLS regression models and the KPLS parameters, such as the number of latent variables, were determined by a 5-fold cross-validation with the root-mean-squared-error. Results obtained by KPLS models are compared with the measured data. It was shown that, under given off-design conditions, the KPLS tool was able to predict the unit load and gas flow with a high degree of accuracy.