Prediction Model of Steam Turbine Exhaust Enthalpy Based on Grey Correlation Analysis Method and GSA-LSSVM

doi:10.3969/j.issn.1000－7229.2016.11.017

Abstract

Abstract: The steam exhaust enthalpy is an essential parameter for the thermal economic diagnosis of steam turbine generator group. We determine the input variables of the model by steam turbine power equation and grey correlation analysis (GCA) method, and optimize the punishment factor μ and nuclear radial range σ of least square support vector machine (LSSVM) by gravitation search algorithm (GSA). The RBF_kernel is selected as the kernel function of LSSVM through the comparative analysis. Based on the GCA-GSA-LSSVM, this paper establishes the mathematical model to predict the exhaust enthalpy of steam turbine, compares it with the BP neural network and RBF neural network, and analyzes its robustness. The results show that the prediction model of steam turbine exhaust enthalpy based on GCA-GSA-LSSVM has the advantages of high precision, strong generalization ability and strong robustness. This method provides a powerful tool for accurately predicting the energy saving potential of the unit.

Key words: least square support vector machine (LSSVM), gravitation search algorithm(GSA), grey correlation analysis method(GCA), steam turbine exhaust enthalpy

CLC Number:

TM 621

WANG Huijie，FAN Zhiyuan，XU Xiaogang，LI Xinxin. Prediction Model of Steam Turbine Exhaust Enthalpy Based on Grey Correlation Analysis Method and GSA-LSSVM[J]. Electric Power Construction, 2016, 37(11): 115-.

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