Fatigue Load Monitoring of an Onshore Wind Turbine

Abstract

Wind turbines are typically designed for an operational life of 20-25 years. The operation of the assets can be extended beyond their design life if structural components have sufficient reserves left. One approach is to monitor fatigue loads and compare these with design assumptions to determine the remaining useful lifetime of the assets. A major challenge is that sensors for measuring the stress history, such as strain gauges, only deliver local information. Monitoring of every hot spot is technically and financially not feasible due to cost and access restrictions. In addition, strain gauges only have a limited lifetime when compared to accelerometers. Several response estimation or extrapolation methods have been proposed in literature to tackle this problem. All of them are Kalman filter based methods, with the exception of the Modal Decomposition and Expansion Method. A new Kalman filter based method has been recently proposed in literature called Gaussian Process Latent Force Model. The aim of this work is to assess this new method with respect to existing ones both theoretically and numerically. Theoretically, the Kalman filter based methods always rely on white gaussian noise assumptions for the unknown loads, and the modal decomposition and expansion method disregard measurement imperfections. The new method improves upon these assumptions by providing a flexible stochastic definition for the unknown load, and by taking into account measurement imperfections. The numerical analysis is restricted to a comparison with respect to the modal decomposition and expansion method given the different theoretical backgrounds. The comparison is realised upon a simulation which illustrates and validates the methods, and upon a real-life onshore wind turbine equipped with accelerometers and strain gauges. Measured strains are compared with estimated strains. The accuracy of each method is quantified using the mean absolute error and by the correlation between measurement and estimation. The higher accuracy obtained shows that the new method is an improvement upon existing methods. This is further extended in the calculation of Damage Equivalent Loads. The result shows a relative error that, depending on operational conditions, ranges within 20-40[%] for the modal decomposition and expansion method and less than 10[%] for the new method. These results show that the novel Gaussian Process Latent Force Model method should be taken into account for response estimation when accuracy is relevant. Future works should aim on developing a mechanical model that better capture the real behaviour of the wind turbine, as the accuracy of the response estimation methods is mainly controlled by the validity of the underlying assumptions of the mechanical model. Furthermore, the strain estimations should be sought in the whole frequency range, this can be realised by including measurements that deliver this information: GPS sensors or inclinometers for example.