Browsing by Author "Würth, Ines"

Now showing 1 - 4 of 4

Open Access
Determination of stationary and dynamical power curves using a nacelle-based lidar system
(2012) Würth, Ines; Rettenmeier, Andreas; Schlipf, David; Cheng, Po Wen; Wächter, Matthias; Rinn, Philip; Peinke, Joachim
This paper investigates the determination of stationary and dynamical power curves using a nacelle-based lidar system. Wind speed measurements on one of the REpower 5MW turbines at the German offshore test site "alpha ventus" were carried out with a pulsed lidar system that is capable of measuring the wind field at different measurement planes over the rotor swept area. The results show that the stationary lidar-based power curve has a small scatter but is shifted towards lower wind speeds compared to a conventional power curve measured with a cup anemometer from a met mast. The new approach of calculating dynamical power curves shows short-time dynamics of the turbine and allows a quick detection of changes such as the icing of an anemometer or the reduction in the maximum power output of the wind turbine.
Open Access
How far do we see? Analysis of the measurement range of long-range lidar data for wind power forecasting
(2017) Würth, Ines; Brenner, Alex; Wigger, Maayen; Cheng, Po Wen
Open Access
IEA Wind Task 32: Wind Lidar : identifying and mitigating barriers to the adoption of wind lidar
(2018) Clifton, Andrew; Clive, Peter; Gottschall, Julia; Schlipf, David; Simley, Eric; Simmons, Luke; Stein, Detlef; Trabucchi, Davide; Vasiljevic, Nikola; Würth, Ines
IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants.
Open Access
Minute-scale forecasting of wind power using long-range lidar data
(2022) Würth, Ines; Cheng, Po Wen (Prof. Dr.)
With the introduction of renewable energies, the power grid has transformed from a centralised to a decentralised system. To balance the supply and demand of power in the energy grid at all times in spite of the volatile nature of wind and solar power, grid operators have to rely on accurate forecasts. However, state of the art wind power forecasting methods are not able to forecast changes of power in the minute-scale accurately. Therefore new methods are needed. This thesis investigates the use of a long-range lidar to forecast wind power on the minute-scale. To that aim, two measurement campaigns were carried out. One was an onshore campaign, where the lidar was installed fixed on a radio tower next to a turbine that a forecast was made for. The second was an offshore campaign where the lidar was installed on top of the nacelle of a wind turbine. Both campaigns lasted over several months and the wind speed was measured in several kilometers in front of the turbine. During this time the turbine`s own data system also recorded the 10-minute average power from the turbine. In this thesis, a wind power forecast process is established. Lidar data is transformed from radial velocity to filtered horizontal wind speed and wind direction. The wind field information is then propagated to the wind turbine with an advection model based on Taylor's hypothesis. The forecasted wind speed at the turbine is then transformed into a forecasted power with the help of the power curve of the turbine. To account for the uncertainty in the wind speed and power forecast, probabilistic forecast methods are applied. The results show that lidar-based forecasts at the offshore site are accurate in a forecast horizon up to ten minutes and outperform the benchmark forecast method persistence. Longer forecast horizons are biased because only small wind speeds measured further away from the wind turbine arrive with a delay of more than ten minutes. At the onshore site, persistence outperforms the lidar-based method in all forecast horizons, includinig the forecast horizon up to 10 minutes. The reason is that the Taylor based advection model does not model the actual propagation at the complex onshore site well enough. During ramp events, the lidar-based forecast demonstrates its strength: information from the wind speed measured a few kilometers in front of the turbine allows us to forecast changes of power. In comparison, persistence only uses old information and therefore cannot forecast any future changes. It is concluded that the added value of using a lidar for minute-scale forecasts lies in forecasting changes of power. As wind ramps are potentially critical to the grid stability, or can affect the cost of balancing the power system if they are not forecast well, using lidars at wind farms to improve the power forecast is advised. However, challenges to the implementation of lidar-based forecasts remain. Lidar measurements depend on the aerosol content in the air and therefore the availability of the measurements for a forecast is not guaranteed. A fallback solution is needed such as statistical models or numerical weather prediction. To achieve forecast horizons of more than 10 minutes, the lidar measurement range needs to be extended beyond 10 kilometers. And to establish lidars as a state-of-the-art forecasting tool, standards are needed, which could be enabled by groups such as the IEA Wind community. Wind lidar data coupled with propagation models and power curves has fundamental advantages for minute-scale wind power forecasting. Although this thesis has shown that current approaches may not be perfect, the rapid pace of wind lidar technology development, the increasing number of users, and the growing network of third party service providers, suggests that wind lidar is the future of minute-scale wind power forecasting.