A method of identifying data by considering the excitation characteristics is proposed to solve the problem that it is difficult to select suitable samples from the historical operation data to identify the turbine work model. Firstly

Fisher's information matrix condition number is applied to extract the excitation characteristics of the historical operating data

which together with the trend characteristics and the correlation between parameters constitute the set of feature variables. Secondly

by using the feature variables as inputs and the identification results generated based on the standard turbine work model as outputs

the Random Forest classification algorithm is used to generate a classification rule model for the identification data to realize the online selection of identification data. Finally

the accuracy of the model classification results and the identification effect of the selected data are verified. The result proves that the accuracy of the classification rule model is 97.561%

which can accurately select the sample segments containing sufficient incentives in the historical operation data

and the identification results of the turbine work model are in high consistency with that of the the standard model.