DocumentCode
620408
Title
Nonlinear distributed MPC strategy with application to AGC of interconnected power system
Author
Huiyun Nong ; Xiangjie Liu
Author_Institution
Sch. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China
fYear
2013
fDate
25-27 May 2013
Firstpage
3935
Lastpage
3940
Abstract
In practical power system, the existence of the valve limit on the governor and the generation rate constraints (GRC) present great challenge to the automatic generation control (AGC). In this paper, the valve limit on the governor is modeled by a T-S model, and a distributed model predictive control (MPC) method is employed to AGC to cope with the generation rate constraints (GRC). Model predictive control is a popular control strategy which takes systematic account of process input, state and output constraints, and distributed MPC is suitable for controlling large-scale networked systems such as power system. This method decomposes the overall system into subsystems, each with its own MPC controller. The proposed methodology is tested with a four-area interconnected power system. Simulation results demonstrate the proposed distributed MPC achieves performance equivalent to centralized MPC.
Keywords
centralised control; distributed parameter systems; networked control systems; nonlinear control systems; power system control; power system interconnection; predictive control; AGC; GRC; T-S model; centralized model predictive control method; four-area interconnected power system; generation rate constraints; governor; large-scale networked controller; nonlinear distributed MPC strategy; output constraints; process input; systematic account; valve limit; Automatic generation control; Frequency control; Mathematical model; Power systems; Predictive models; Valves; AGC; T-S model; distributed MPC;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location
Guiyang
Print_ISBN
978-1-4673-5533-9
Type
conf
DOI
10.1109/CCDC.2013.6561637
Filename
6561637
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