Campaign Planning Tool

Wednesday 10 Apr 19
The digital campaign planning tool full description and demonstration on 3 sites is available in a discussion paper in Wind Energy Science journal.


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:

1. Based on a preliminary wind farm layout, we generate optimum measurement positions using a greedy algorithm and a measurement ’representative radius’;

2. Areas where the lidar cannnot or should not be installed (due to terrain constraints such as lakes or line-of-sight  blockage due to the terrain) are excluded - this determines the possible positions for the first lidar;

3. Possible positions fro the second lidar are defined based on the constraints related to the angles between theliar beams to abotine reliable measurements;

4.  A trajectory through the measurement positions is generated for each lidar beam by applying the travelling salesman problem (TSP).

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. 

The full paper  (currently under revision) can be found here.

Our plan is to release the Python code as open access code in the fall 2019.