Dynamic modeling of wind turbine generation system based on grey-box identification with genetic algorithm

The data-based parameter identification is an efficient way for the modeling of Wind Turbine Generation System (WTGS) which only reveals the external characteristic. However the inner characteristic is unknown. In practice the parameters of the mechanism model are changing with time. Thus in the paper the grey-box modeling approach which utilizes the operation data and operating mechanism together are proposed to realize the online parameter identification of WTGS under closed-loop. Considering the essential difference between aerodynamic system and the other systems the driven-train system and the electrical system are identified together which also simplify the identification process and the switching mechanism is proposed for the parameters update of mechanism model which is much efficient and applicable in practice. Besides the identification of driven-train system and electrical system in combination could ensure the global approximation ability to the dynamics of WTGS while the local approximation ability to the subsystems could also be validated when the identification parameters are obtained. In the paper the combined state space model including the driven-train model and Doubly-Fed Induction Generator (DFIG) model is deduced. The GH Bladed Software would be used to generated the operation data for identification and the genetic algorithm is adopted to identify the model parameters. As a result the nonlinear state space model could be obtained and its approximation ability to the dynamic characteristic of WTGS is validated through simulation. Then the practical characteristic of wind turbine could be approximated more accurately. Note that the paper provides meaningful research basis for the control design and model-based fault diagnosis of large-scale WTGS.