Small signal stability assessment of grid‑connected system for grid‑following/grid‑forming hybrid new energy stations based on TCN

Authors

Tao LIN, Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaFollow
Zhengyang LIN, Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Chen LI, Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Jun LI, Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan University, Wuhan 430072, China; School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Keywords

new energy station; grid-following control; grid-forming control; small signal stability; time convolutional neural network

Abstract

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.

DOI

10.19781/j.issn.1673-9140.2024.04.020

First Page

169

Last Page

177

Recommended Citation

LIN, Tao; LIN, Zhengyang; LI, Chen; and LI, Jun (2024) "Small signal stability assessment of grid‑connected system for grid‑following/grid‑forming hybrid new energy stations based on TCN," Journal of Electric Power Science and Technology: Vol. 39: Iss. 4, Article 20.
DOI: 10.19781/j.issn.1673-9140.2024.04.020
Available at: https://jepst.researchcommons.org/journal/vol39/iss4/20