Digitizing scanning lidar measurement campaign planning

Abstract. Multiple wind measurements is a way to reduce the uncertainty of wind farm energy yield assessments by reducing the extrapolation distance between measurements and wind turbines locations. A WindScanner system consisting of two synchronized scanning lidar potentially represents a cost-effective solution for multi-point measurements, especially in complex terrain. However, the system limitations and limitations imposed by the wind farm site are detrimental to the installation of scanning lidars and the number and location of the measurement positions. To simplify the process of finding suitable measurement positions and associated installation locations for the WindScanner system we have devised a campaign planning workflow. The workflow consists of four phases. In the first phase, based on a preliminary wind farm layout, we generate optimum measurement positions using a greedy algorithm and a measurement 'representative radius'. In the second phase, we create several Geographical Information System (GIS) layers of information such as exclusion zones, line-of-sight (LOS) blockage, and lidar range maps. These GIS layers are then used in the third phase to find optimum positions of the WindScanners with respect to the measurement positions considering the WindScanner measurement uncertainty. In the fourth phase, we optimize and generate trajectory through the measurement positions by applying the traveling salesman problem (TSP) on these positions. The above-described workflow has been digitized into the so-called Campaign Planning Tool (CPT) currently provided as a Python library which allows users an effective way to plan measurement campaigns with WindScanner systems. In this study, the CPT has been tested on three different sites characterized by different terrain complexity and wind farm dimensions and layouts. The CPT has shown instantly whether the whole site can be covered by one system or not.