Machine learning techniques for load monitoring of offshore wind turbines

Abstract

In the race for cost reduction in the offshore wind industry, support structure optimization leading to weight reduction plays a prominent role. The fatigue limit state is often the driving consideration for support structure design. Monitoring the monopile loads can offer an accurate knowledge of its consumed and remaining fatigue lifetime, which in turn has the potential benefits of lifetime extension and feedback on current design practices among others. Since within an offshore wind farm not all turbines are subjected to the same loading, a turbine or cluster-specific internal load monitoring scheme is investigated using data-driven approaches and specifically feedforward artificial neural networks (ANNs) and linear regression (LR).

The purpose of this thesis is to examine whether it is possible to accurately estimate the actual loading of the offshore wind turbine at the monopile mudline fatigue sensitive location by utilizing standard signals and/or sensor measurements and machine learning techniques instead of a structural model.

Data simulated with the Siemens Gamesa Renewable Energy in-house aeroelastic code - Bonus Horizontal axis wind turbine Code (BHawC) – is used, including operation and idling fatigue load cases. Ten minute time and frequency-domain statistics of signals that are collected in turbines are used as inputs to the data-driven models, whereas 10-minute damage equivalent loads (DELs) of monopile moments are used as targets. By using statistics of rotor rotational velocity, electrical power output, blade pitch angle, hub wind speed and nacelle accelerations, a mean absolute error of DEL estimation smaller than 4% in both operation and idling load cases is achieved, under the condition that the training set properly reflects the entire variation in environmental conditions. Furthermore, errors average out over multiple 10-minute intervals, resulting in accurate long-term estimation with residual errors between -1% and +1%. The accuracy of the estimation can be further improved by including additional sensors; accelerometers placed at the tower bottom (TB) can help reduce ANN mean absolute error by up to 40% in operational load cases.

In addition, if inclinometers are installed at TB they can allow 10-minute equivalent ranges of rotation signals at TB to be used as inputs to a LR scheme to accurately estimate DELs. When the 10-minute intervals are binned based on whether the mean wind speed is below, around, or above the turbine rated wind speed, this method gives mean absolute errors below 2.5%. As a result, TB inclinometers are recommended as extra sensors for load monitoring, provided that factors such as their accuracy level and durability compared to alternatives are considered.

The data-driven models examined in this thesis show potential not only for online internal load monitoring, but also for fast estimation of past load histories using recorded data.