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Gap metric–based model bank construction for wind turbine predictive control
Author(s) -
Li Dewen,
Chen Zhe,
Li Ning
Publication year - 2018
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2429
Subject(s) - turbine , model predictive control , control theory (sociology) , metric (unit) , controller (irrigation) , wind speed , measure (data warehouse) , range (aeronautics) , wind power , computer science , engineering , control engineering , control (management) , meteorology , electrical engineering , database , artificial intelligence , biology , aerospace engineering , mechanical engineering , agronomy , operations management , physics
Summary This paper presents a novel model bank construction method for the multiple model predictive control of wind turbine system. The gap metric is used to measure the dynamic difference between the linearized models of the wind turbine system at different wind speed. Two algorithms are then proposed to divide the wind speed range in different operating regions. Meanwhile, a complete and nonredundant linear model bank is established to approximate the wind turbine system in the whole operating region. We take the robust model predictive control algorithm to design the local controller and utilize the wind speed as the switching criterion to combine the submodels. The simulation study on a 5‐MW wind turbine verifies the efficiency of the proposed method.