An investigation on the impact of intermittency on wind Lidar profiler data utilizing a Universal Multifractal framework

The intermittent nature of turbulence introduces significant variability and extreme events, which profoundly complicates efforts to accurately measure, model, and predict its behavior. In the context of the atmospheric boundary layer, this intermittent turbulence can lead to localized bursts of wind shear, which poses risks to wind energy operations. These fluctuations directly impact the operational efficiency and structural integrity of wind turbines. Specifically, turbulence influences fatigue loads on the turbines and is essential for accurately modeling wake effects that occur within wind farms, which can affect the performance of adjacent turbines and forecasting energy production. Research suggests that a multifractal framework characterized by complex patterns across various scales enables one to properly model the intermittency of turbulence. To investigate this phenomenon, the present study analyzes wind data collected using a state-of-the-art lidar (Light Detection and Ranging) system profiler. This profiler was deployed on an offshore measurement mast situated near an offshore wind farm located 13 kilometers off the coast of Fécamp, France. Employing a universal multifractal (UM) framework, this study seeks to simulate and analyze the extreme variability inherent in the collected data. In the first step, The UM framework will be used to quantify the effects of intermittency on standard metrics such as turbulence intensity (TI) and spectral slopes, also accounting for the resolution at which they are computed and the frequency of data. Empirical estimates of TI and spectral slope in homogeneous turbulence often deviate from theoretical scaling, which can be theoretically and empirically quantified. In the second step, the results of the UM analysis of the measured time series will be discussed. Additionally, this study will delve into the instrumental biases introduced by the lidar instrument used in the measurement of turbulence. These biases can significantly impact the accuracy of data interpretation and reliability of results, making it essential to explore and address them thoroughly. This research not only addresses the theoretical aspects of turbulence but also has practical implications for optimizing wind energy operations in the face of unpredictable environmental conditions. Finally, the authors would like to acknowledge the partial financial support of the French Government, managed by the Agence Nationale de la Recherche under the Investissements d’Avenir program, with the reference ANR-10-IEED-0006-34. This work was carried out in the framework of the NEMO project.