基于共生生物搜索算法的汽轮机最优初压研究

doi:10.3969/j.issn.1000-1158.2019.03.16

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Abstract To obtain the optimal initial pressure of a steam turbine under loads,a heat rate forecasting model was established using fast symbiotic organisms search (FSOS) algorithm and extreme learning machine (ELM). The model was compared with BP neural network, symbiotic organisms search (SOS) algorithm’s optimizing extreme learning machine (ELM) and fast symbiotic organisms search (FSOS) algorithm’s optimizing support vector machine (SVM). Then, FSOS was employed to seek the optimal initial pressure and the main steam flow based on the forecasting model to make the heat rate under each load the lowest. Finally, an optimal initial pressure curve was fitted by the optimized main steam pressure and compared with the factory designed sliding pressure curve. The results showed that the average heat rate decreases about 58.51 kJ·(kW· h)^-1 according to the optimal initial pressure curve, which improved the energy conversion efficiency of the unit and had a significant effect on the economic operation of the steam turbine.

Key words： metrology heat rate steam turbine optimal initial pressure symbiotic organisms search algorithm extreme learning machine

Received: 02 January 2018 Published: 19 April 2019

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TB941

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	Articles by authors
	NIU Pei-feng
	WANG Xiao-fei
	LIU Nan
	CHANG Ling-fang
	ZHANG Xian-cheng

Cite this article:

NIU Pei-feng,WANG Xiao-fei,LIU Nan, et al. Optimization on Inital Pressure of a Steam Turbine Based on Symbiotic Organisms Search Algorithm[J]. Acta Metrologica Sinica, 2019, 40(3): 447-454.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.03.16 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I3/447

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