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

2014年第8卷第4期

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《能源前沿（英文）》 >> 2014年第8卷第4期 doi: 10.1007/s11708-014-0335-5

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

School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China

录用日期： 2014-12-03 发布日期： 2015-01-09

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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.

关键词

optimization ; turbine ; cold-end system ; BP neural network ; genetic algorithm