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| Investigation on Multi-model Modeling Method of Steam Turbine Heat Rate |
| NIU Pei-feng1,2,LIU Chao1,LI Guo-qiang1,MA Yun-fei1,CHEN Gui-lin1,2,ZHANG Xian-chen1,2 |
1.Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China;
2.National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, Hebei 066004, Chin |
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Abstract Taking into account the problem that the heat rate of steam turbine is difficult to accurately calculate, a novel heat rate multi-model soft measurement methodology based on kernel fuzzy c-means and shuffled frog-leaping algorithm optimized least squares support vector machine(LS-SVM is proposed), which is employed to calculate the heat rate under different working conditions. This method applies kernel fuzzy c-means algorithm clustering heat rate data. Taking the mean error of 5-fold cross-validation as fitness value of parameters selection for LS-SVM, LS-SVM based on SFLA is trained and established local model for each cluster, and then the model output is obtained by the switch way, so as to realize the heat rate multi-model method. Compared with the single LS-SVM model and BP network heat rate prediction model, the multi-model has a higher prediction accuracy and better generalization ability.
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2015, Vol. 36 
