Recursive Subspace-based Model Refinement Method for Digital Twin of Thermal Power Unit
Due to factors such as simplified assumptions or equipment characteristic deviation,modeling errors are inevitable in the mechanism modeling of thermal power units.To deal with the problem,this paper proposes a novel model refinement method based on recursive subspace for the digital twin of thermal power units.Firstly,the digital twin models are built based on mechanism analysis and combined with small sample data of typical conditions,ensuring interpretability and generalization performance.Secondly,based on the recursive subspace identification method,the refinement model is built and updated online in real time to compensate for the modeling error,improving the prediction accuracy and ensuring the high fidelity of the overall digital twin model.Finally,simulation results validate the effectiveness of the proposed method.
digital twinthermal power unitmodel refinementsubspace identificationdata driven
NETL
NSTL
万方数据
维普
