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Feedback Linearization Based Distributed Model Predictive Control for Secondary Control of Islanded Microgrid
Author(s) -
Guo Zhuoyu,
Li Shaoyuan,
Zheng Yi
Publication year - 2020
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1906
Subject(s) - control theory (sociology) , microgrid , model predictive control , controller (irrigation) , plug and play , feedback linearization , linearization , control (management) , stability (learning theory) , convergence (economics) , control engineering , computer science , nonlinear system , engineering , physics , quantum mechanics , artificial intelligence , machine learning , agronomy , economics , biology , economic growth , operating system
In this paper, a distributed Model Predictive Control (DMPC) is proposed for the secondary voltage and frequency control of islanded microgrid, where each distributed generator (DG) is controlled by a Model Predictive Control (MPC) in the secondary control layer, individually. With considering the nonlinear dynamics of DG with primary control, input‐output feedback schemes are developed for voltage and frequency control separately. Then, all MPCs use the local and neighboring nodes information to solve the optimization problem instead of communicating with a central controller. In this way, the control of the whole system is fully distributed, which allows for a plug‐and‐play. The convergence and stability analysis of the overall closed‐loop system are provided. The simulation result shows the effectiveness of the proposed method.