To establish an accurate and effective dynamic model of cogeneration units

a modeling method based on digital twin technology is proposed using unit operation data. Firstly

the historical data stored in the unit data server is extracted

it is then clustered using the improved genetic simulated annealing fuzzy C-means method to establish a historical data clustering library. Then

during the operation of the unit

real-time operational data is collected and transmitted

and a multi-level similarity recognition strategy is used to retrieve the historical data closest to real-time operational data in the historical data clustering library. Then

based on the optimization

the extreme learning machine will use the searched historical data for unit modeling. Finally

a twin model of a cogeneration unit in Hangzhou is established and comparative experiments are conducted. The results show that

the built model meets the accuracy requirements and can track the real-time state response of the unit. The model accuracy can be further optimized by flexibly changing the parameter settings during the modeling process.