Wind farm dynamic equivalent modeling by GA-optimized GRU-LSTM-FC combined network

Aiming at the dynamic equivalent modeling of a wind farm depends on its operation mode and specific disturbance,which is difficult to obtain a general equivalent model,a data-driven modeling method based on gate recurrent unit-long short term memory-full connection(GRU-LSTM-FC) combined network is proposed,and the genetic algorithm(GA)-based method is proposed to optimize the combined network model. Firstly,the wind turbine is described as a set of differential-algebraic equations. The model input is the wind speed,wind direction at the anemometer tower,and the time series of voltage at the point of common coupling(PCC),and the model output is the time series of the wind farm power. Then,by comparing the similarity between the LSTM／GRU network structure with memory ability and the differential equation of wind turbine,and the similarity between the FC network structure and the algebraic equation of wind turbine,an equivalent modeling method of wind farm based on GRU-LSTM-FC combined network is proposed. In order to optimize the combined model,GA is applied to optimize the number of FC layers and the number of neurons at each layer in the combined network. Finally,taking a wind farm as an example,the feasibility of data-driven equivalent modeling with the proposed combined network is verified,and the proposed method is compared with other neural network models,and the advantages of the proposed model are analyzed. © 2023 Electric Power Automation Equipment Press. All rights reserved.