反向建模方法在汽轮机热耗率建模中的应用

Application of Reversed Modeling Method in Prediction of Steam Turbine Heat Rate

针对热耗率与其影响因素之间存在的复杂非线性关系,提出了基于自适应混沌反学习万有引力算法(ACOGSA)和最小二乘支持向量机(LSSVM)的汽轮机热耗率反向建模方法.利用某600 MW超临界汽轮机组运行数据,采用基于LSSVM的反向建模方法建立热耗率预测模型,采用ACOGSA算法解决LSSVM的模型参数优化问题,并与GSA-LSSVM模型和BP神经网络模型的预测结果进行比较.结果表明:所建立的模型比传统模型具有更好的泛化能力,更能准确地预测汽轮机的热耗率.

To investigate the nonlinear relationship between steam turbine heat rate and the affecting factors,a reversed modeling method was proposed for the heat rate based on adaptive chaotic opposition-based learning gravitational search algorithm(ACOGSA)and least squares support vector machine(LSSVM),following which a heat rate prediction model was established with operation data of a 600 MW supercritical steam turbine unit using LSSVM reversed modeling method.ACOGSA algorithm was used to solve the problem of LSSVM optimal parameters,and the prediction results were compared with those of the GSALSSVM model and BP neural network model.Results show that the proposed model has better generalization ability than traditional models,which therefore can predict the heat rate of steam turbine more accurately.