To support the rapid switching of unit control modes in grid-following/grid-forming hybrid new energy stations and achieve safe and stable operation of these stations that can adapt to changes in grid strength,a rapid assessment method for small-signal stability of grid-connected systems in grid-following/grid-forming hybrid new energy stations based on temporal convolutional network (TCN) is proposed. Specifically,an aggregated impedance model for grid-following/grid-forming hybrid new energy stations is constructed,and the small-signal stability margin of the grid-connected system is obtained through eigenvalue calculations. Furthermore,using the short-circuit ratio of the grid-connected system and the information on the grid-following/grid-forming control mode of the new energy station as input features,and the small-signal stability margin and damping ratio of the grid-connected system as output features,a TCN is trained to obtain a rapid assessment model for small-signal stability of grid-connected systems in hybrid new energy stations. The trained model can quickly output the corresponding small-signal stability margin and damping ratio based on the short-circuit ratio and the control mode of each unit in the grid-following/grid-forming hybrid new energy station. A case study is conducted using a new energy station with 10 wind turbines,and the results show that compared to the long short-term memory neural network method,the proposed method reduces the mean absolute percentage error of small-signal stability margin prediction and damping ratio prediction by 16.76％ and 14.75％ respectively. Additionally,the computation time of the proposed method is reduced by 98.54％ compared to the eigenvalue calculation method,verifying the accuracy and efficiency of the proposed rapid assessment method for small-signal stability.
关键词
新能源场站/跟网型控制/构网型控制/小干扰稳定/时间卷积神经网络
Key words
new energy station/grid-following control/grid-forming control/small signal stability/time convolutional neural network
维普
万方数据