Current power performance methods focus mainly on individual turbines and involve large investments. A common example is the use of a costly meteorological mast located closely to the turbine of interest. This approach is insufficient for accurate power performance assessments of wind farms covering a large area.
Fortunately, new cost-effective monitoring technologies have emerged which may assist in power performance throughout large wind farms, such as spinner anemometers and nacelle LiDAR systems. However, these technologies need further standardisation and industry acceptance. Particularly, performance in waked conditions, repeatability, interchangeability and stability of these technologies still need to be proven.
In SAWOP we will develop methodologies to monitor the performance of individual turbines and the wind farm as a whole by the application of nacelle LiDAR and spinner sensor technologies. The results will be used to determine the performance of the windfarm and the sensors itself in free-stream and wake conditions. Based on possible performance deficits, optimised control settings will be derived. In the last stage of the project, innovative wind farm control strategies will be investigated. It is expected that the information from the applied sensors will further improve the performance by using wind farm control.
A measurement campaign will be carried out at the onshore Vattenfall windfarm Klim Fjordeholme in Denmark. This farm consists of 21 Siemens turbines of 3.2MW each with a rotor diameter of 113 m. In the campaign, a profiling LiDAR will scan the incoming wind field and act as a reference. Eight turbines will be equipped with ROMO Wind iSpin spinner anemometers, and two forward-facing nacelle LiDAR’s from different manufacturers will be installed. Finally, a scanning LiDAR will map the wake conditions enhanced by a wind field reconstruction algorithm. Special care is taken to calibrate all sensors following the latest standards in order to ensure an objective measure of turbine and sensor performance. All together, these measurements form a unique data set to evaluate the performance of the sensors in free-stream and waked conditions.
This project aims to demonstrate that the overall energy yield of large wind farms can be increased through the use of advanced measuring equipment and related advanced control strategies. The project will result in improved wind farm performance optimization strategies using spinner anemometers and nacelle LiDAR input. The outcomes from this work will provide valuable input towards internationally accepted standardisation of power performance assessment using these new sensor technologies.
The additional tangible result is the realisation of a unique measurement database. The partners in the project will draw learnings from this database regarding the wind flow in an operational wind farm, turbine power performance and monitoring systems in waked conditions. This will highly benefit future wind farm designs.
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