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

Yuexin Song; Yizhi Chen; Chenghong Tang; Wei Wang; Hao Xiao; Wei Pei; Yanhong Yang

doi:10.1109/ICPSAsia58343.2023.10295042

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

Yuexin Song
, Yizhi Chen
, Chenghong Tang
, Wei Wang
, Hao Xiao
, Wei Pei
, Yanhong Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1905-1911
Number of pages	7
ISBN (Electronic)	9798350339345
DOIs	https://doi.org/10.1109/ICPSAsia58343.2023.10295042
State	Published - 2023
Externally published	Yes
Event	2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023 - Chongqing, China Duration: 7 Jul 2023 → 9 Jul 2023

Publication series

Name	2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023

Conference

Conference	2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023
Country/Territory	China
City	Chongqing
Period	7/07/23 → 9/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

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

Access to Document

10.1109/ICPSAsia58343.2023.10295042

Cite this

Song, Y., Chen, Y., Tang, C., Wang, W., Xiao, H., Pei, W., & Yang, Y. (2023). Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions. In 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023 (pp. 1905-1911). (2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPSAsia58343.2023.10295042

Song, Yuexin ; Chen, Yizhi ; Tang, Chenghong et al. / Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions. 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1905-1911 (2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023).

@inproceedings{2d6f7d4f32794a9aa69c6177fccdfac6,

title = "Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions",

abstract = "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.",

keywords = "Climbing control strategy for wind turbines, Extreme weather, Short-term power forecasting",

author = "Yuexin Song and Yizhi Chen and Chenghong Tang and Wei Wang and Hao Xiao and Wei Pei and Yanhong Yang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023 ; Conference date: 07-07-2023 Through 09-07-2023",

year = "2023",

doi = "10.1109/ICPSAsia58343.2023.10295042",

language = "英语",

series = "2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1905--1911",

booktitle = "2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023",

}

Song, Y, Chen, Y, Tang, C, Wang, W, Xiao, H, Pei, W & Yang, Y 2023, Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions. in 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023. 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1905-1911, 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023, Chongqing, China, 7/07/23. https://doi.org/10.1109/ICPSAsia58343.2023.10295042

Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions. / Song, Yuexin; Chen, Yizhi; Tang, Chenghong et al.
2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1905-1911 (2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions

AU - Song, Yuexin

AU - Chen, Yizhi

AU - Tang, Chenghong

AU - Wang, Wei

AU - Xiao, Hao

AU - Pei, Wei

AU - Yang, Yanhong

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Climbing control strategy for wind turbines

KW - Extreme weather

KW - Short-term power forecasting

UR - https://www.scopus.com/pages/publications/85177826815

U2 - 10.1109/ICPSAsia58343.2023.10295042

DO - 10.1109/ICPSAsia58343.2023.10295042

M3 - 会议稿件

AN - SCOPUS:85177826815

T3 - 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023

SP - 1905

EP - 1911

BT - 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023

Y2 - 7 July 2023 through 9 July 2023

ER -

Song Y, Chen Y, Tang C, Wang W, Xiao H, Pei W et al. Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions. In 2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1905-1911. (2023 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2023). doi: 10.1109/ICPSAsia58343.2023.10295042

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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