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Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model

Author

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  • Songjune Lee

    (Wind Energy Research Team, Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), 200 Haemajihaean-ro, Gujwa-eup, Jeju 63357, Republic of Korea)

  • Seungjin Kang

    (Wind Energy Research Team, Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), 200 Haemajihaean-ro, Gujwa-eup, Jeju 63357, Republic of Korea)

  • Gwang-Se Lee

    (Wind Energy Research Team, Jeju Global Research Center (JGRC), Korea Institute of Energy Research (KIER), 200 Haemajihaean-ro, Gujwa-eup, Jeju 63357, Republic of Korea)

Abstract

In recent years, the demand and requirement for renewable energy have significantly increased due to concerns regarding energy security and the climate crisis. This has led to a significant focus on wind power generation. As the deployment of wind turbines continues to rise, there is a growing need to assess their lifespan and improve their stability. Access to accurate load data is crucial for enhancing safety and conducting remaining life assessments of wind turbines. However, maintaining and ensuring the reliability of measurement systems for long-term load data accumulation, stability assessments, and residual life evaluations can be challenging. As a result, numerous studies have been conducted on load prediction for wind turbines. However, existing load prediction models based on 10 min statistical data cannot adequately capture the short-term load variations experienced by wind turbines. Therefore, it is essential to develop models capable of predicting load with a high temporal resolution to enhance reliability, especially with the increasing scale and development of floating wind turbines. In this paper, we developed prediction models with a 50 Hz resolution for the bending strain at the tower bottom of offshore wind turbines by combining SCADA data and acceleration data using machine learning techniques and analyzed the results. The load prediction models demonstrated high accuracy, with a mean absolute percentage error below 4%.

Suggested Citation

  • Songjune Lee & Seungjin Kang & Gwang-Se Lee, 2023. "Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model," Energies, MDPI, vol. 16(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4922-:d:1178237
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    References listed on IDEAS

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    1. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    2. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
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    Cited by:

    1. Qianlong Zhu & Wenjing Xiong & Haijiao Wang & Xiaoqiang Jin, 2023. "Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data," Energies, MDPI, vol. 16(20), pages 1-17, October.

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