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The distributed energy system is an energy supply method built around the end users, which can achieve energy sustainability and reduce emissions compared to traditional centralized energy systems. The micro gas turbine (MGT)-based combined cooling and power (CCP) system has received renewed attention as an important distributed energy system technology due to its substantial energy savings and reduced emission levels. The task of the MGT-CCP system is to quickly adapt to changes in various renewable energy sources to maintain the balance in energy supply and demand in a distributed energy system. Therefore, it is imperative to improve the load tracking capability of the MGT-CCP system with advanced control technologies toward achieving this goal. However, the difficulty of controlling the MGT-CCP system is that the MGT responds very fast while CCP responds very slowly. To this end, the dynamic characteristics and nonlinear distribution of the MGT and CCP processes are analyzed, and a coordinated predictive control strategy is proposed by utilizing the generalized predictive control for the MGT system and the Hammerstein generalized predictive control for the CCP system. The coordinated predictive control of generalized predictive control and Hammerstein generalized predictive control was implemented in an 80 kW MGT-CCP simulator to verify the effectiveness of the proposed method. The simulation results show that compared with PID and MPC, the proposed control method not only can greatly improve simultaneous cooling and power load-following capability, but also has the best control effect when accessing with renewable energy.
Chen Chen; Jiangfan Lin; Lei Pan; Kwang Y. Lee; Li Sun. Improving Simultaneous Cooling and Power Load-Following Capability for MGT-CCP Using Coordinated Predictive Controls. Energies 2019, 12, 1180 .
AMA StyleChen Chen, Jiangfan Lin, Lei Pan, Kwang Y. Lee, Li Sun. Improving Simultaneous Cooling and Power Load-Following Capability for MGT-CCP Using Coordinated Predictive Controls. Energies. 2019; 12 (6):1180.
Chicago/Turabian StyleChen Chen; Jiangfan Lin; Lei Pan; Kwang Y. Lee; Li Sun. 2019. "Improving Simultaneous Cooling and Power Load-Following Capability for MGT-CCP Using Coordinated Predictive Controls." Energies 12, no. 6: 1180.
The control of an ultra-supercritical (USC) boiler–turbine power plant is critical in maintaining the safety of the sustainable power grid. However, it is challenging due to the internal nonlinearity, hard manipulation constraints, and widespread uncertainties. To this end, a fuzzy extended state observer (FESO)-based stable fuzzy predictive control (SFPC) approach is developed in this paper. First, the control difficulties of the USC boiler–turbine unit are analyzed. Then, based on a Takagi–Sugeno (T–S) fuzzy model, a new FESO is developed for nonlinear systems to achieve a more precise observation performance. The gain of FESO is determined by solving a series of linear matrix inequalities, while guaranteeing the stability of FESO. Then, by combining the proposed FESO with the SFPC, an integrated FESO–SFPC algorithm is devised. The disturbance rejection ability of the FESO–SFPC algorithm is analyzed theoretically. Simulation results on a 1000 MW USC boiler–turbine power plant model further validate the effectiveness of the proposed method.
Chen Chen; Lei Pan; Shanjian Liu; Li Sun; Kwang Y. Lee. A Sustainable Power Plant Control Strategy Based on Fuzzy Extended State Observer and Predictive Control. Sustainability 2018, 10, 4824 .
AMA StyleChen Chen, Lei Pan, Shanjian Liu, Li Sun, Kwang Y. Lee. A Sustainable Power Plant Control Strategy Based on Fuzzy Extended State Observer and Predictive Control. Sustainability. 2018; 10 (12):4824.
Chicago/Turabian StyleChen Chen; Lei Pan; Shanjian Liu; Li Sun; Kwang Y. Lee. 2018. "A Sustainable Power Plant Control Strategy Based on Fuzzy Extended State Observer and Predictive Control." Sustainability 10, no. 12: 4824.
It is extremely challenging to control ultra-supercritical boiler–turbine unit on account of its nonlinearity, coupling among multi-variables, hard input constraints, and unknown disturbances. An offset-free output-feedback stable model predictive control (MPC) (OFOF-SMPC) for nonlinear constrained ultra-supercritical boiler-turbine units is investigated. It is shown that the OFOF-SMPC is composed of a disturbance and state observer, a steady-state target calculator as well as a stable MPC, which can remove unknown disturbances from output channels and ensure the stability of the closed-loop system with input constraints. Simulation results demonstrate the effectiveness of the proposed algorithm.
Chen Chen; Lei Pan; Jiong Shen; Kwang Y. Lee; Fan Zhang; Li Sun; Xiao Wu; Junli Zhang; Wenchao Xue. Control of Nonlinear Constrained Ultra-Supercritical Boiler–Turbine Units Using Offset-Free Output-Feedback Stable MPC. IFAC-PapersOnLine 2018, 51, 155 -160.
AMA StyleChen Chen, Lei Pan, Jiong Shen, Kwang Y. Lee, Fan Zhang, Li Sun, Xiao Wu, Junli Zhang, Wenchao Xue. Control of Nonlinear Constrained Ultra-Supercritical Boiler–Turbine Units Using Offset-Free Output-Feedback Stable MPC. IFAC-PapersOnLine. 2018; 51 (28):155-160.
Chicago/Turabian StyleChen Chen; Lei Pan; Jiong Shen; Kwang Y. Lee; Fan Zhang; Li Sun; Xiao Wu; Junli Zhang; Wenchao Xue. 2018. "Control of Nonlinear Constrained Ultra-Supercritical Boiler–Turbine Units Using Offset-Free Output-Feedback Stable MPC." IFAC-PapersOnLine 51, no. 28: 155-160.