Posters

Abstract

Abstract: Optimizing the operation and maintenance of wind turbines is crucial as the wind energy sector continues to expand. Predicting the mass imbalance of wind turbines, which can seriously damage the rotor blades, gearbox, and other components, is one of the key issues in this field. In this paper, we propose a machine learning-based method for predicting the mass imbalance of wind turbines utilizing information from multiple sensors and monitoring systems. We collected and analysed data from a wind turbine located at Fraunhofer Institute of Wind Energy Systems. The data included various parameters such as wind speed, blade root bending moments, rotor speed, and nacelle acceleration. We used this data to train and test machine learning classification models based on different algorithms, including extra-tree classifiers, support vector machines, and gradient boosting. Our results showed that the machine learning models were able to predict the mass imbalance of wind turbines with high accuracy. Particularly, the extra tree classifiers outperformed all others with an F1 score of 0.89 and an accuracy of 90%. Additionally, we examined the significance of various features in predicting the mass imbalance and observed that the rotor speed and blade root bending moments were the most crucial variables. Our research has significant effects for the wind energy sector since it offers a reliable and efficient way for predicting wind turbine mass imbalance. Wind farm operators can save maintenance costs, minimize downtime of wind turbines, and increase the lifespan of turbine components by identifying and eliminating mass imbalances. Also, further investigation will allow us to apply our method to different kinds of wind turbines, and it is simple to incorporate into current monitoring systems. In conclusion, our study demonstrates the potential of machine learning for predicting the mass imbalance of wind turbines. We believe that our approach can significantly benefit the wind energy industry and contribute to the development of sustainable energy sources.