基于磷虾群算法的汽轮机热耗率建模应用

Modelling of Turbine Heat Rate Based on Krill Herd Algorithm and Its Application

为了准确建立汽轮机热耗率预测模型,以某热电厂600MW超临界汽轮机组为研究对象,采用基于反向学习自适应的磷虾群算法(OAKH)和快速学习网(FLN)进行综合建模,并将该模型的预测结果与基本快速学习网、粒子群算法、生物地理学优化算法和磷虾群算法优化的快速学习网模型的预测结果进行比较.结果表明:OAKH算法能够更好地优化FLN模型参数,使所建立的FLN汽轮机热耗率预测模型具有更高的预测精度和更强的泛化能力,能够准确、有效地预测热电厂的汽轮机热耗率.

To accurately predict the heat rate of steam turbine, a model was established with the sample da- ta of a 600 MW supercritical steam turbine unit in a thermal power plant using opposition adaptive krill herd algorithm （OAKH） and fast learning network （FLN）, of which the prediction results were compared with that of basic FLN model and those FLN models whose parameters were optimized by particle swarm optimization, biogeography-based optimization and krill herd algorithm. Results show that compared with other algorithms and models, the model of turbine heat rate based on OAKH algorithm has a higher accu- racy in prediction and stronger capability in parameter optimization and generation, which may help to ac- curately and effectively predict the heat rate of steam turbines.