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

Songjune Lee, Seungjin Kang and Gwang-Se Lee ()
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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

Energies, 2023, vol. 16, issue 13, 1-18

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%.

Keywords: offshore wind turbine; bending strain prediction; structural health monitoring; machine learning; long short-term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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