IEA Wind Task 32: Wind lidar identifying and mitigating barriers to the adoption of wind lidar

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

Wind Speed Preview Measurement and Estimation for Feedforward Control of Wind Turbines

Wind turbines typically rely on feedback controllers to maximize power capture in below-rated conditions and regulate rotor speed during above-rated operation. However, measurements of the approaching wind provided by Light Detection and Ranging (lidar) can be used as part of a preview-based, or feedforward, control system in order to improve rotor speed regulation and reduce structural loads. But the effectiveness of preview-based control depends on how accurately lidar can measure the wind that will interact with the turbine. In this thesis, lidar measurement error is determined using a statistical frequency-domain wind field model including wind evolution, or the change in turbulent wind speeds between the time they are measured and when they reach the turbine. Parameters of the National Renewable Energy Laboratory (NREL) 5-MW reference turbine model are used to determine measurement error for a hub-mounted circularly-scanning lidar scenario, based on commercially-available technology, designed to estimate rotor effective uniform and shear wind speed components. By combining the wind field model, lidar model, and turbine parameters, the optimal lidar scan radius and preview distance that yield the minimum mean square measurement error, as well as the resulting minimum achievable error, are found for a variety of wind conditions. With optimized scan scenarios, it is found that relatively low measurement error can be achieved, but the attainable measurement error largely depends on the wind conditions. In addition, the impact of the induction zone, the region upstream of the turbine where the approaching wind speeds are reduced, as well as turbine yaw error on measurement quality is analyzed. In order to minimize the mean square measurement error, an optimal measurement prefilter is employed, which depends on statistics of the correlation between the preview measurements and the wind that interacts with the turbine. However, because the wind speeds encountered by the turbine are unknown, a Kalman filter-based wind speed estimator is developed that relies on turbine sensor outputs. Using simulated lidar measurements in conjunction with wind speed estimator outputs based on aeroelastic simulations of the NREL 5-MW turbine model, it is shown how the optimal prefilter can adapt to varying degrees of measurement quality

Reducing LIDAR Wind Speed Measurement Error with Optimal Filtering

Abstract — Recent research has shown the potential for reduction in wind turbine generator speed error and structural loads with the introduction of feedforward control using preview LIDAR measurements. Several sources of error exist in the estimation of the wind speeds that will interact with the turbine rotor, including LIDAR distortion and coherence loss due to wind evolution. If a feedforward controller is designed assuming perfect wind speed measurements, however, the error in the disturbance estimate may cause feedforward control to increase output errors. Here we derive the minimum mean square error feedforward controller for imperfect measurements using statistical descriptions of the wind. We show that the resulting controller is the ideal feedforward controller, assuming perfect measurements, in series with a Wiener prefilter to reduce the mean square error of the disturbance estimate. We derive the optimal filter in the frequency domain assuming infinite preview as well as the optimal filter in the time domain with preview time constraints. Examples illustrating the error reduction with optimal prefiltering are provided for simulated control and measurement scenarios. I

A Spectral Model for Evaluating the Effect of Wind Evolution on Wind Turbine Preview Control

Abstract — As wind turbines become larger and more flexible, the potential benefits of load mitigating control systems become more important to reduce fatigue and extend component life. In the last five years, there has been significant research activity exploring the effectiveness of preview control techniques that may be feasible using advanced wind measurement technologies like LIDAR (light detection and ranging). However, most control development tools use Taylor’s frozen turbulence hypothesis. The end result is that preview measurements made up-stream from the rotor can be obtained with unrealistic accuracy, because the same wind velocities eventually arrive at the turbine. In this study, we extend the spectral methods commonly used to generate turbulent wind fields for controls simulation, but in a way that emulates wind evolution. This changes preview measurements made upwind from the rotor, in such a way that the differences – between the preview measurements and speeds arriving at the turbine – increase with distance from the rotor. We then evaluate the degradation in load mitigation performance of a controller that uses preview measurements obtained at various distances in front of the rotor. I

Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms

A recent expert elicitation showed that model validation remains one of the largest barriers for commercial wind farm control deployment. The Gaussian-shaped wake deficit model has grown in popularity in wind farm field experiments, yet its validation for larger farms and throughout annual operation remains limited. This article addresses this scientific gap, providing a model comparison of the Gaussian wind farm model with historical data of three offshore wind farms. The energy ratio is used to quantify the model’s accuracy. We assume a fixed turbulence intensity of I∞=6% and a standard deviation on the inflow wind direction of σwd=3° in our Gaussian model. First, we demonstrate the non-uniqueness issue of I∞ and σwd, which display a waterbed effect when considering the energy ratios. Second, we show excellent agreement between the Gaussian model and historical data for most wind directions in the Offshore Windpark Egmond aan Zee (OWEZ) and Westermost Rough wind farms (36 and 35 wind turbines, respectively) and wind turbines on the outer edges of the Anholt wind farm (110 turbines). Turbines centrally positioned in the Anholt wind farm show larger model discrepancies, likely due to deep-array effects that are not captured in the model. A second source of discrepancy is hypothesized to be inflow heterogeneity. In future work, the Gaussian wind farm model will be adapted to address those weaknesses