基于数字孪生技术的热电联产机组建模研究

Research on modeling of cogeneration units based on digital twin technology

为建立精确有效的热电联产机组动态模型,利用机组运行数据,提出一种基于数字孪生技术的热电联产机组建模方法。首先提取机组数据服务器内存储的历史数据,采用改进遗传模拟退火的模糊C均值方法对其进行聚类,建立历史数据聚类库;然后在机组运行期间,采集并传输实时运行数据,利用多级相似度识别策略,在历史数据聚类库内检索最接近实时运行数据的历史数据;接着基于优化的极限学习机将搜寻到的历史数据用于机组建模;最后以杭州某热电联产机组为实验对象,建立该机组的孪生模型并进行对比实验。结果表明:所建模型满足精确性要求,能够跟踪机组实时状态响应;并且可以通过灵活改变建模过程中参数的设定来进一步优化模型精度。

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.