Unit Initial Pressure Optimization Based on Gravity Search Algorithm and LSSVM

doi:10.11930/j.issn.1004-9649.201810094

Abstract

Abstract: The main steam pressure is an important parameter that affects the thermal economy of steam turbine. In order to accurately determine the optimal initial pressure of steam turbine during sliding pressure operation, a hybrid method of steam turbine initial pressure optimization based on gravity search algorithm (GSA) and least square support vector machines (LSSVM) is proposed in this paper. Firstly, with the aid of LSSVM the regression forecasting model is established for the heat rate of steam turbine, in which GSA algorithm is developed to find the optimal parameters of LSSVM to improve the regression accuracy and generalization capability for heat rate forecast. Then, based on the established model, the GSA algorithm is re-applied to seek the optimal main steam pressure with respect to the minimum heat consumption rate under each load level. Finally, taking a 600 MW supercritical steam turbine as the research object, the simulation results show that through optimization search the proposed method can be employed to provide a satisfactory initial pressure value for the main steam operation.

Key words: steam turbine, optimal initial pressure, gravity search algorithm, LSSVM, optimization

CLC Number:

TK267

HU Jian, LIU Chao. Unit Initial Pressure Optimization Based on Gravity Search Algorithm and LSSVM[J]. Electric Power, 2019, 52(6): 160-165.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201810094

https://www.electricpower.com.cn/EN/Y2019/V52/I6/160

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