PG 9351FA燃气-蒸汽联合循环机组性能劣化研究

Investigation on performance degradation of PG 9351FA based gas-turbine combined cycle unit

为定量分析燃气-蒸汽联合循环机组性能劣化程度,提出机组在不同负荷下的性能-时间劣化研究模型。基于PG 9351FA型联合循环机组历史运行数据,利用GA-BP神经网络建立各年份燃气轮机效率及联合循环效率预测模型。在THERMOFLEX仿真软件中建立联合循环全工况物理模型,研究压气机和透平不同劣化程度对系统性能的影响。结果表明:基于数据驱动的系统效率预测模型的测试集相对误差均在3.5%以内,且相关系数R值均高于0.9,模型泛化能力较好;同一负荷下燃气轮机效率和联合循环效率逐年降低,机组A级检修对其高负荷工况下的效率有较明显恢复;透平劣化对机组性能的影响大致为压气机劣化的2倍。本文工作可为劣化机理的研究提供理论指导,具有工程应用价值。

To quantitatively analyzing the degradation degree of performance of gas-turbine combined cycle(GTCC)units,the performance-time degradation research model for the unit at different load is proposed.On the basis of historical operation data of PG 9351FA GTCC unit,GA-BP neural network is adopted to establish the prediction models for gas turbine efficiency and GTCC efficiency in each year.Moreover,physical model for the GTCC overall characteristics is established in THERMOFLEX simulation software,to investigate the influence of compressor and gas turbine with different degradation degrees on the system performance.The results show that,the relative error of prediction of test set based on the data-driven efficiency model is within 3.5%and the correlation coefficient Rexceeds 0.9,which demonstrates the good generalization ability of the neural network.Both the gas turbine efficiency and GTCC efficiency at the same unit load decline gradually year by year,while they are apparently recovered at high load after A-class overhaul.The influence of turbine performance degradation on system performance is approximately twice as large as that of the compressor degradation.The work provides theoretical guidance to the study of degradation mechanism,which has engineering application value.