@inproceedings{e4005f305a0742f1abfc685376201a11,
title = "Model Predictive Control Strategy of Wind Turbine Based on LSTM",
abstract = "Model Predictive Control is an advanced control method commonly applied in industrial control systems. The basic idea is to combine the existing model and use the state and constraints of the current time system to predict the behavior state and output variables of the period of time in the future, so as to achieve more accurate and efficient dynamic regulation. Based on MPC in this paper, the MPC method based on Long Short- Term Memory is introduced to control the machine side and network side of wind turbine and compared with MPC method to verify the speed and stability of MPC method based on LSTM. The findings indicate that the approach utilizing LSTM MPC can adapt to the system changes more intelligently and quickly, accurately complete the control goal of the wind turbine, ensure that the wind turbine can achieve efficient and stable operation state, and significantly improve the stability and efficiency of the wind farm grid connection system.",
keywords = "LSTM, Model Predictive Control, Wind turbine, deep learning, insert, stability of grid-connected system",
author = "Yan Liu and Lin Zhu and Huihui Song and Tao Deng and Panpan Yang and Weimin Wu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 11th International Forum on Electrical Engineering and Automation, IFEEA 2024 ; Conference date: 22-11-2024 Through 24-11-2024",
year = "2024",
doi = "10.1109/IFEEA64237.2024.10878647",
language = "英语",
series = "2024 11th International Forum on Electrical Engineering and Automation, IFEEA 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1050--1053",
booktitle = "2024 11th International Forum on Electrical Engineering and Automation, IFEEA 2024",
address = "美国",
}