Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions

被引：0

作者：

Song, Yuexin ^{[1
]}

Chen, Yizhi ^{[2
]}

Tang, Chenghong ^{[2
]}

Wang, Wei ^{[2
]}

Xiao, Hao ^{[3
]}

Pei, Wei ^{[3
]}

Yang, Yanhong ^{[3
]}

机构：

[1] Chinese Acad Sci, Grid Technol Res Grp, Inst Elect Engn, Beijing, Peoples R China

[2] NARI Technol Co Ltd, State Grid Elect Power Res Inst, NARI Grp Corp, State Key Lab Smart Grid Protect & Control, Nanjing, Peoples R China

[3] Chinese Acad Sci, Inst Elect Engn, Beijing, Peoples R China

来源：

2023 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA, I&CPS ASIA | 2023年

关键词：

Extreme weather; Short-term power forecasting; Climbing control strategy for wind turbines;

D O I：

10.1109/ICPSASIA58343.2023.10295042

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Extreme weather poses a great challenge to the safe operation of wind power and new power systems dominated by wind power. Providing accurate wind power prediction will be an effective response. For this reason, this paper proposed a short-term power forecasting for wind power generation under extreme weather conditions. Firstly, the relationship between wind speed and wind turbine output power is analyzed and a wind power generation model is established. Then, the Long Short-Term Memory Neural Network (LSTM) is applied to construct a short-term power prediction model for wind turbines, and establishes three models for wind turbine decommissioning under extreme weather. Moreover, the climbing control strategy of wind turbine is also investigated to guarantee the system safety, stability and operation economy. Finally, the numerical analysis is carried out on the wind farm consists of 28 wind turbines, the results verify the effectiveness and superiority of the proposed method.

引用

页码：1905 / 1911

页数：7

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