Abstract
In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method.
Highlights
The steam/water loop is an important part of a steam power plant, which plays a role in feed water supply and recycling processes
The results indicate that the distributed model predictive control (DiMPC) may achieve similar performance as centralized model predictive control (CMPC) while outperforming the decentralized model predictive control (DeMPC) method
A comparison is conducted between the decentralized model predictive control (DeMPC), the CMPC and the DiMPC, and the results show the effectiveness of the DiMPC
Summary
The steam/water loop is an important part of a steam power plant, which plays a role in feed water supply and recycling processes. Processes 2019, 7, 442 to deal with the constraints in the steam/water loop, model predictive control is selected to maintain good performance for each sub-loop. Such methods are readily matured for various applications in the Industry 4.0 paradigm [5]. The input rates are measured in percentage per second
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