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Frontiers in Energy

Front. Energy    2014, Vol. 8 Issue (4) : 459-463     https://doi.org/10.1007/s11708-014-0335-5
RESEARCH ARTICLE |
Optimization of turbine cold-end system based on BP neural network and genetic algorithm
Chang CHEN(),Danmei XIE,Yangheng XIONG,Hengliang ZHANG
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
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Abstract

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.

Keywords optimization      turbine      cold-end system      BP neural network      genetic algorithm     
Corresponding Authors: Chang CHEN   
Online First Date: 03 December 2014    Issue Date: 09 January 2015
 Cite this article:   
Chang CHEN,Danmei XIE,Yangheng XIONG, et al. Optimization of turbine cold-end system based on BP neural network and genetic algorithm[J]. Front. Energy, 2014, 8(4): 459-463.
 URL:  
http://journal.hep.com.cn/fie/EN/10.1007/s11708-014-0335-5
http://journal.hep.com.cn/fie/EN/Y2014/V8/I4/459
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Chang CHEN
Danmei XIE
Yangheng XIONG
Hengliang ZHANG
Fig.1  Structure of BP neural network
Sample No. P DwA DwB tw1 tw2 IcpA IcpB IvpA IvpB pc,LP pc,HP Pnet
Training samples 1 401.1 30853.6 30412.2 16.4 22.4 260.7 257.0 266.0 287.0 3.45 3.59 397.3
2 501.4 33803.5 32915.5 16.4 23.1 262.6 255.7 264.7 287.0 3.36 3.50 497.8
3 600.0 33215.6 32686.8 16.3 24.3 261.3 257.1 263.4 284.4 3.38 3.56 596.5
4 700.8 34182.8 33616.5 16.6 25.5 262.7 258.3 263.0 285.1 3.59 3.81 696.7
5 799.9 34194.4 33301.4 16.6 26.6 264.7 257.7 263.5 285.3 3.73 3.96 795.8
6 899.7 34703.1 34104.4 16.6 27.4 263.0 258.5 263.4 287.5 3.82 4.06 895.5
7 960.0 35246.5 34360.0 16.6 27.9 265.3 258.6 261.5 286.7 3.90 4.15 955.8
8 1000.0 35726.2 35028.4 16.6 28.1 264.2 259.1 263.9 286.0 3.92 4.17 995.7
Calibration samples 1 429.8 30486.1 29739.4 16.4 22.9 260.3 254.1 263.5 285.9 3.39 3.52 426.3
2 491.4 33260.3 32323.0 16.4 23.1 262.9 255.5 264.6 287.1 3.36 3.50 487.9
3 592.1 33473.7 25852.5 16.3 24.1 260.1 255.2 263.7 287.4 3.37 3.55 588.5
4 671.7 35378.8 34794.1 16.3 24.6 260.6 256.3 262.6 287.3 3.41 3.60 667.9
5 728.9 34707.6 34078.5 16.6 25.6 262.7 257.9 263.5 287.9 3.62 3.84 724.8
6 791.3 35827.9 35227.1 16.6 26.0 263.5 259.0 262.9 287.1 3.67 3.89 787.0
7 872.7 34655.1 33992.5 16.6 27.2 262.2 257.2 264.0 285.9 3.79 4.03 868.6
8 991.1 35732.0 34960.8 16.6 28.0 264.6 258.9 263.5 286.3 3.92 4.17 986.8
Tab.1  Training samples (part) and calibration samples of BP neural network
Fig.2  Trained BP neural network
Fig.3  Relative error of training and calibration samples
Fig.4  Fitness value and current best individual

(a) Best and mean fitness value; (b) current best individual

Pressure/kPa Load ratio/%
40 50 60 70 80 90 100
pc,LP 3.36 3.31 3.31 3.53 3.65 3.76 3.92
pc,HP 3.61 3.55 3.62 3.89 4.01 4.09 4.21
Tab.2  Optimal condenser pressure pc,LP and pc,HP under different conditions
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