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http://dx.doi.org/10.5855/ENERGY.2014.23.4.263

A Study of the Feature Classification and the Predictive Model of Main Feed-Water Flow for Turbine Cycle  

Yang, Hac Jin (School of Robot & Automation, Dongyang Mirae Univ.)
Kim, Seong Kun (School of Mechanical Engineering, Hoseo Univ.)
Choi, Kwang Hee (Korea Hydraulics and Nuclear Corporation)
Publication Information
Journal of Energy Engineering / v.23, no.4, 2014 , pp. 263-271 More about this Journal
Abstract
Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.
Keywords
Main Feed-Water; Feature Classification; Support Vector Machines; Turbine Cycle;
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