Master's thesis: Derivation of an offshore wind field from surface wind measurement data from two scanning lidars

Task description:

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 several scanning lidars (Dual Doppler) in an operating offshore wind farm of EnBW provides the measurement data that serves as the basis for the proposed master's thesis. It is planned to derive a 2D wind field from measurement data of planar Plan Position Indicator (PPI) scans.

The PPI scan carried out with scanning lidar measures radial wind speeds in a previously defined area. By overlapping the PPI scans from two scanners, two components of the wind field can be determined in one grid and the horizontal wind speed and wind direction can be derived. The measurement was carried out with a range of up to 8 km.

The aim of the proposed master's thesis is to create a two-dimensional wind field from the existing measurement data from the offshore measurement campaign in the German Bight. The focus is on the following aspects:

• Implementation of evaluation routines in Python to analyze the PPI measurement data

• Evaluation of the results with regard to the uncertainty of the radial wind speed measurements and the propagation of these uncertainties into the derived variables horizontal wind speed and wind direction

• Development of proposals for the use of the different scan modes for lidar and AI-supported methods for large-scale measurement of the wind field

Work steps:

• Literature research on lidar and existing evaluation algorithms

• Development of a theoretical chapter to classify your 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

Prerequisites:

• Degree in natural sciences or engineering

• Interest in data analysis and initial programming skills, ideally in Python

• Interest in renewable energies

• Good knowledge of German or English

The position is initially limited to 6 months and will be remunerated.

Please send a letter of motivation, CV and overview of current academic achievements to tabea.hildebrand[at]uni-kassel[dot]de.