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Master thesis: Derivation of an offshore wind field from areal wind measurement data of two scanning lidars
As part of the BMWK-funded research project "Development of a Lidar- and AI-supported method for large-scale measurement of the wind field inside and outside offshore wind farms" (Window), a lidar- and AI-supported method for large-scale measurement of wind fields is being developed. A measurement campaign with multiple scanning lidars (dual Doppler) in an operating offshore wind farm of EnBW provides the measurement data that serve as the basis for the proposed master thesis. It is planned to derive a 2D wind field from measurement data of plan position indicator (PPI) scans.
The PPI scan performed with scanning lidar measures radial wind speeds in a predefined area. By overlapping the PPI scans from two scanners, two components of the wind field can thus be determined in a grid and the horizontal wind speed and wind direction can be derived. The measurement was performed with a range of up to 8 km.
The aim of the proposed master thesis is to create a two-dimensional wind field from the already existing measurement data of the offshore measurement campaign in the German Bight. The focus is on the following aspects:
Implementation of evaluation routines in Python for the analysis of the PPI measurement data.
Evaluation of the results with respect to the uncertainty of the radial wind speed measurements and the propagation of these uncertainties into the derived quantities horizontal wind speed and wind direction
Development of proposals for the use of the different scanning modes for lidar and AI-based methods for large-scale measurement of the wind field
Literature review on lidar and existing evaluation algorithms.
Development of a theoretical chapter for the classification of the own work in the scientific discourse
Development of a concept for the implementation of a script for the evaluation of PPI scans
Statistical evaluation, comparison of the measurement data with reference measurements from the wind farm
Visualization and discussion of the results as well as summary and evaluation
Study of natural or engineering sciences
Interest in data analysis and first programming skills, ideally in Python
Interest in renewable energies
Good knowledge of German or English
The position is initially limited to 6 months and is remunerated.
Please send letter of motivation, CV and overview of current academic achievements to tabea.hildebrand[at]uni-kassel[dot]de.