Machine Learning-Based Prediction of 2 MW Wind Turbine Tower Loads During Power Production Based on Nacelle Behavior

Soichiro Kiyoki, Shigeo Yoshida () and Mostafa A. Rushdi
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Soichiro Kiyoki: Hitachi Ltd., 6-6, Marunouchi 1-Chome, Chiyoda-ku, Tokyo 100-8280, Japan
Shigeo Yoshida: Institute of Ocean Energy, Saga University, 1 Honjomachi, Saga 840-8502, Japan
Mostafa A. Rushdi: Research Institute for Applied Mechanics, Kyushu University, 6-1 Kasugakoen, Kasuga 816-8580, Japan

Energies, 2025, vol. 18, issue 1, 1-26

Abstract: The cost of a wind turbine support structure is high and this support structure is difficult to repair, especially for offshore wind turbines. As such, the loads and stresses that occur during the actual operation of wind turbines must be monitored from the perspective of maintenance planning and lifetime prediction. Strain measurement methods are generally used to monitor the load on a structure and are highly accurate, but their widespread implementation across all wind turbines is impractical due to cost and labor constraints. In this study, a method for predicting the tower load was developed, using simple measurements applied during power generation, for onshore wind turbines. The method consists of a machine learning model, using the nacelle displacement and nacelle angle as inputs, which are highly correlated with loads at the bottom of the tower. Nacelle displacements can be derived from accelerations, which are already monitored in regard to most wind turbines; the nacelle angle can be calculated from the nacelle angle velocity, measured with a gyroscope. The low-frequency components that cannot be captured with these parameters were predicted using the operational condition data used for wind turbine control. Additionally, the prediction accuracy was increased by creating and integrating separate machine learning models for each typical vibration component. The method was evaluated through the aeroelastic simulation of a 2 MW wind turbine. The results showed that the fatigue and extreme loads of the fore–aft and side–side bending moments at the bottom of the tower can be predicted using operational conditions and nacelle accelerations, and the prediction accuracy in regard to the high-frequency components can be increased by adding the nacelle angle velocity into the model. Furthermore, the fatigue loads of the torsional torque can be evaluated using the nacelle angle velocity. The proposed method has the ability to predict the loads at the bottom of the tower without any, or with only a few, additional sensors.

Keywords: wind turbine; load; machine learning; linear regression; extra trees regressor; nacelle behavior (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: 2025
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