Low Voltage Ride through Research on Distributed Deep Neural Network-Based Doubly Fed Induction Generator

The low voltage ride through performance of doubly fed induction generator (DFIG) not only depends on the control strategy, but also depends on the selection of control parameters. Due to the time-consuming optimization algorithm of control parameters, it cannot achieve the corresponding effect in real-time control. Therefore, this paper proposes a method based on offline parameter optimization and model training, as well as online fault identification. Firstly, a large number of different types of fault data are obtained through the established DFIG grid connection model, and offline optimization of control parameters is carried out based on the fault type to form the corresponding low voltage ride through mode. Then, different fault data are classified to form training samples for neural network. At the moment of a power grid fault, the fault data can be directly used to quickly judge the fault type by the trained distributed deep neural network (DNN), and appropriate control strategies can be selected based on the fault type. The feasibility of this method and its advantages in control effectiveness and speed are verified through fault identification and parameter optimization method of doubly fed fan model.