Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning

Abstract

In wind turbines, to investigate the cause of failures and evaluate the remaining lifetime, it may be necessary to measure their loads. However, it is often difficult to do so with only strain gauges in terms of cost and time, so a method to evaluate loads by utilizing only simple measurements is quite useful. In this study, we investigated a method with machine learning to estimate hub center loads, which is important in terms of preventing damage to equipment inside the nacelle. Traditionally, measuring hub center loads requires performing complex strain measurements on rotating parts, such as the blades or the main shaft. On the other hand, the tower is a stationary body, so the strain measurement difficulty is relatively low. We tackled the problem as follows: First, machine learning models that predict the time history of hub center loads from the tower top loads and operating condition data were developed by using aeroelastic analysis. Next, the accuracy of the model was verified by using measurement data from an actual wind turbine. Finally, individual pitch control, which is one of the applications of the time history of hub center loads, was performed using aeroelastic analysis, and the load reduction effect with the model prediction values was equivalent to that of the conventional method.