Optimization of turbine cold-end system based on BP neural network and genetic algorithm

被引：7
作者：
Chen C. [1 ]
Xie D. [1 ]
Xiong Y. [1 ]
Zhang H. [1 ]
机构：
[1] School of Power and Mechanical Engineering, Wuhan University, Wuhan
来源：
Frontiers in Energy | 2014年 / 8卷 / 4期
关键词：
Bp neural network; Cold-end system; Genetic algorithm; Optimization; Turbine;
D O I：
10.1007/s11708-014-0335-5
中图分类号：
学科分类号：
摘要：
The operation condition of the cold-end system of a steam turbine has a direct impact on the economy and security of the unit as it is an indispensible auxiliary system of the thermal power unit. Many factors influence the cold-end operation of a steam turbine; therefore, the operation mode needs to be optimized. The optimization analysis of a 1000 MW ultra-supercritical (USC) unit, the turbine cold-end system, was performed utilizing the back propagation (BP) neural network method with genetic algorithm (GA) optimization analysis. The optimized condenser pressure under different conditions was obtained, and it turned out that the optimized parameters were of significance to the performance and economic operation of the system. © Higher Education Press and Springer-Verlag Berlin Heidelberg 2014.
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收藏
页码：459 / 463
页数：4
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