Exploration of LiDAR measured turbulence via met mast comparison, fatigue analysis & Measure Correlate Predict

Abstract

Several factors play a role in the ongoing process of the acceptance and eventual universal use of LiDAR remote sensing technology in wind site assessment. Especially turbulence parameter 푇퐼 is still giving food for thought. Three important aspects of LiDAR measured 푇퐼 that are considered key are picked out and feature in this work, which attempts to contribute to the handling of LiDAR measured turbulence. Firstly, the accuracy in measuring several wind characteristics compared to met masts,which have been the standard in wind science and industry for the past centuries, is an issue. This thesis evaluates a LiDAR met mast comparison for a Dutch onshore wind farm site to support the handling of the biases between both sources, with a focus on 푇퐼. It finds LiDAR suitable for basic wind measurements, but it still struggles with measuring turbulence accurately. Patterns in biases are exposed and explained and propositions of an alteration of the internal LiDAR correction factor 퐶, as well as the use of a new turbulence parameter called transience 휏m, are made. The second factor addressed in this thesis is an indirect one and originates from the fact that turbulence, and so 푇퐼, plays an important role in the fatigue lifetime damage of a wind turbine. It is tried to answer the question what influence small biases in 푇퐼 have on the fatigue lifetime damage of the tower and blades of a common wind turbine by means of a simulation based sensitivity study. Increments with an order of magnitude found in the first part of this work form an input for this. It is found that the fatigue damage due to stresses from certain bending moments is higher than others and that the blades and tower are affected differently by turbulence. The third factor is related to the inherent flexibility of LiDAR devices. This forms a big advantage over met masts in general and also possibly paves the way for short-term measurement campaigns by LiDAR. It is proven that 푇퐼 shows intra-annual variability, which would therefore require extrapolation techniques. The Measure Correlate Predict (MCP) methodology is chosen to conduct a case study on the application of several MCP algorithms on 푇퐼 data, containing several experiments. It was found that the linear regression method (LR) is unsuitable for making long-term turbulence predictions, but that the variance ratio method (VR) shows promising results on the used performance metrics. It seems that it does not matter whether the short-term data length is 3 or 6 months and that the picked season does not harm the final result significantly.