Main steam parameters of heat recovery steam generator(HRSG)are crucial for the healthy operation of the combined cycle unit.Aiming at the problem of low prediction accuracy of main steam parameters due to non-linearity and time-delay ofoperating parameters of HRSG,a model for predicting main steam parameters of the combined cycle HRSG is proposed.Firstly,operation data of a gas turbine combined cycle were collected to determine the input variables by grey correlation analysis.Secondly,the feature information of the input parameters was extracted by KPCA and the input dimensions were selected according to the principal component contribution ratio.Finally,BiGRU was optimised by IPOA and KPCA-IPOA-BiGRU was constructed to conduct the prediction test of the three-pressure-HRSG main steam parameters.The results show that the proposed model reduces the 28-dimensional input parameters to 8 dimensions and can predict the three-pressure steam parametersusing 8000 data points as the training set,and 2000 data points as the test set.The R²is greater than 98%for all of the tests,which provides technical support for monitoring time-delayed main steam parameters.