| CPC F03D 7/045 (2013.01) [F03D 7/048 (2013.01); F03D 9/25 (2016.05); F05B 2220/706 (2013.01); F05B 2270/20 (2013.01); H02J 2203/20 (2020.01)] | 5 Claims |

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1. A data-driven wind farm frequency control method based on dynamic mode decomposition, wherein the method comprises the following steps:
S1: building a state equation of a power generation unit in a wind farm;
S2: mapping state data in the state equation to a high-dimension observation state vector through an observation function, thereby obtaining a wind turbine high-dimension linear dynamic model through a matrix algebra operation;
S3: according to a dynamic mode decomposition method in the step S2, obtaining a dynamic model of the power generation unit in a wind site; and further defining a state vector in a central control model while defining a wind speed of the power generation unit and an input vector of an active instruction, thereby obtaining a central wind farm control model;
S4: according to a wind farm active frequency modulation instruction and a wind turbine rotation speed fluctuation degree, designing a control optimization objective; and obtaining a constraint condition of a central state vector based on the optimization objective, thus building a complete wind farm frequency dynamic optimization control method;
S5: providing, by a controller, a control command to the wind farm based on the control optimization objective determined by the controller; and
S6: controlling an output frequency of the wind farm in response to the control command, wherein the state equation of the power generation unit in the step S1 is indicated by formula (1):
ωk+1=ƒ(ωk,uk)
Wherein, ωk denotes a wind turbine rotation speed at time k, ƒ denotes a nonlinear state transition relation function and an input variable uk is defined as
![]() Wherein, Pref,k is an active instruction of an external input and νw,k is a current wind speed;
a state transition relation of the power generation unit is mainly comprised in a data pair having a timing sequence correspondence relation, which is denoted as:
X=[x1x2 . . . xN], Y=[y1y2 . . . yN] (3)
wherein
![]() is a data pair at time k, with N pairs in total.
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