Digital twin-based subspace model predictive control for thermal power plant

Abstract

In this article, a digital twin-based subspace model predictive control scheme is proposed for solving model mismatch caused by parameter perturbations of thermal power plants. First, the rational range of essential parameters in the nonlinear model is identified based on the nominal data of an actual 600 MW power plant, which provides necessary foundation for subsequent studies. Second, to improve the load-changing capacity under parameter disturbances, a novel digital twin-based subspace model predictive control algorithm is established by regarding the crucial parameter as extended input of predictive model. Finally, the small fluctuations during steady-state operation are effectively restrained through transforming weights and constraints slowly and smoothly at the end of load change. Simulation results illustrate that compared with the conventional subspace-based model predictive control, the proposed digital twin-based subspace model predictive control scheme can obtain a better performance in terms of both dynamic regulation rate and steady-state performance under parameter perturbations.

A generalized data assimilation architecture of digital twin for complex process industrial systems

“…To maintain consistency with the true states, DT needs to obtain real-time measurements from sensors to calibrate the digital states, which is exactly what DA can do [20]. In the general five-dimensional DT model (as shown in figure 1) proposed by Thelen et al [19], updating the digital state (physical to virtual) through measurements is an important dimension in the DT framework.…”