基于改进狮群算法的汽轮机热耗率模型预测

doi:10.3969/j.issn.1000-1158.2021.07.04

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Abstract Aiming at the problem that the model of steam turbine heat consumption rate is difficult to be predicted accurately, a method based on the improved lion group algorithm and fast learning network integrated modeling was proposed. Firstly, the traditional lion swarm optimization algorithm is prone to premature convergence and the slow speed of the algorithm in the late iteration leads to the algorithm falling into the local optimal defect. The algorithm was improved by introducing the tabu search, the nonlinear disturbance factor and the golden sine strategy. Secondly, the improved lion swarm algorithm was numerically validated, and the results showed that it had the higher convergence accuracy and the faster convergence speed. Finally, a prediction model of steam turbine heat consumption rate was established based on the operation data of steam turbine in a thermal power plant, and a fast learning network optimized by improved lion swarm optimization algorithm was used to predict the heat consumption rate. The experimental results were compared with other optimization strategies, and the results showed that the fast learning network prediction model based on improved lion swarm algorithm had the higher generalization ability and improved the prediction accuracy of steam turbine heat consumption rate.

Key words： metrology prediction of heat consumption rate lion swarm optimization fast learning network tabu search golden sine nonlinear perturbation factor

Received: 30 October 2019 Published: 15 July 2021

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TB941

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	WANG Chan-chan

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WANG Chan-chan. Prediction of Steam Turbine Heat Consumption Rate Based on Improvement Lion Swarm Optimization[J]. Acta Metrologica Sinica, 2021, 42(7): 853-860.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.07.04 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I7/853

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