Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042

Editorial for the special issue "remote sensing of atmospheric conditions forwind energy applications"

This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations are presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented

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

U.S. Department of Energy Workshop Report - Research Needs for Wind Resource Characterization

This workshop brought the different atmospheric and wind technology specialists together to evaluate research needs for wind resource characterization

Can a dual pulsed lidar system measure the lateral coherence of turbulence?

Characterizing the coherence of turbulence in the marine boundary layer faces a significant challenge due to the limited availability of offshore measurements within the relevant altitude ranges, particularly up to 250 m. The coherence of turbulence describes the spatial correlation of wind velocity fluctuations, which is a key parameter for determining environmental loading on wind turbines. The turbulent wind loading represents one of the main uncertainties faced by the offshore wind engineering sector. Uncertainties arising from turbulent wind loading represent major challenges in the offshore wind engineering sector. Advancements in remote sensing technology, such as Doppler wind lidars, have opened new possibilities for studying wind turbulence at heights relevant to the increasing size of wind turbine rotors. This master’s thesis presents an analysis of 15 days of wind records collected by two pulsed wind lidars and two sonic anemometers during the COTUR project at Obrestad lighthouse, located on the southwestern coast of Norway. The site is expected to predominantly represent offshore conditions. The primary objective of this study is to assess the capability of pulsed Doppler wind lidar instruments in capturing the lateral co-coherence of turbulence along the wind component. Wind records obtained by the sonic anemometers mounted on 11 m high masts are used as reference data. The analysis focuses on both single and two-point statistics of wind turbulence, with particular emphasis on studying the co-coherence of turbulence. The results revealed that wind sectors aligned with northerly or southerly wind directions are suitable for comparison studies. In these wind directions, a good agreement is found between the two different instruments. Comparing co-coherence estimates obtained from pulse lidar and sonic anemometer showed negligible differences, indicating that spatial averaging did not significantly affect the estimation of co-coherence. By assessing the ability of pulsed Doppler wind lidar instruments to capture turbulence co-coherence, this study contributes to the applicability of lidar technology for characterizing turbulence and its potential for improving assessments of environmental loading on offshore wind turbinesMasteroppgave i energiENERGI399I5MAMN-ENE

Innovative measurement techniques for atmospheric turbulence and wind energy

The measurement of different atmospheric flow quantities is of utmost importance for a correct understanding of most atmospheric phenomena. Researchers and industry in the fields of meteorology and wind engineering demand extensive and accurate measurements of atmospheric turbulence for a better understanding of its role in a wide range of applications such as weather forecast, wind resource evaluation, wind turbine wake, pollutant transport or urban climate. Quantitative measurements of relevant variables are particularly valuable for the development, testing and validation of turbulence parameterizations used in both analytical and numerical models. This thesis focuses in the development of innovative measurement techniques for atmospheric turbulence, particularly suitable for wind energy applications, and it is divided into four different studies. The first study presents a multirotor UAV-based technique for the measurement of atmospheric turbulence and temperature. The technique is based on the integration of a fast-response multi-hole pressure probe and a thermocouple with an inertial measurement unit (IMU). This technique allows for an accurate measurement of time series of the three components of the velocity vector and temperature at any point in the atmosphere in which the UAV can fly. The technique relies on the correction of the velocity vector measured by the pressure probe on the frame of reference of the UAV -non inertial- with the information provided by the IMU. The study includes a validation of the technique against sonic anemometry and the measurement of the signature of tip vortices shed by the blades of a full-scale wind turbine as an example of its potential. The second study presents a triple-lidar technique developed for the measurement of atmospheric turbulence at a point in space from synchronous measurements of three intersecting Doppler wind lidars. The laser beams must be non-coplanar so that trigonometric relationships allow the reconstruction of the velocity vector. The technique is validated against sonic anemometry in terms of the instantaneous velocity vector, turbulence statistics, Reynolds stresses and the spectra of the three components of the velocity and the turbulent kinetic energy. The third study investigates the theoretical accuracy of the reconstruction of a full-scale wind turbine wake in terms of the average and the standard deviation of the longitudinal velocity component by volumetric scans from lidar measurements. To that end, a series of virtual experiments are performed, where synthetic lidar measurements are obtained from LES simulation results. The methodology described quantifies the errors and allows the optimization of the scan pattern so that it balances the different error sources and minimizes the total error. The fourth study presents a measurement campaign dedicated to the characterization of full-scale wind turbine wakes under different inflow conditions. The measurements are performed with two nacelle-mounted scanning lidars. The first lidar characterizes the inflow while the second performs horizontal planar scans of the wake. The relationships obtained for the growth rate of wake width, velocity recovery and length of the near wake are compared to analytical models and allow to correct the parameters prescribed until now with new, more accurate values directly derived from full-scale experiments

Statistical Modeling to Support Power System Planning

This dissertation focuses on data-analytic approaches that improve our understanding of power system applications to promote better decision-making. It tackles issues of risk analysis, uncertainty management, resource estimation, and the impacts of climate change. Tools of data mining and statistical modeling are used to bring new insight to a variety of complex problems facing today’s power system. The overarching goal of this research is to improve the understanding of the power system risk environment for improved operation, investment, and planning decisions. The first chapter introduces some challenges faced in planning for a sustainable power system. Chapter 2 analyzes the driving factors behind the disparity in wind energy investments among states with a goal of determining the impact that state-level policies have on incentivizing wind energy. Findings show that policy differences do not explain the disparities; physical and geographical factors are more important. Chapter 3 extends conventional wind forecasting to a risk-based focus of predicting maximum wind speeds, which are dangerous for offshore operations. Statistical models are presented that issue probabilistic predictions for the highest wind speed expected in a three-hour interval. These models achieve a high degree of accuracy and their use can improve safety and reliability in practice. Chapter 4 examines the challenges of wind power estimation for onshore wind farms. Several methods for wind power resource assessment are compared, and the weaknesses of the Jensen model are demonstrated. For two onshore farms, statistical models outperform other methods, even when very little information is known about the wind farm. Lastly, chapter 5 focuses on the power system more broadly in the context of the risks expected from tropical cyclones in a changing climate. Risks to U.S. power system infrastructure are simulated under different scenarios of tropical cyclone behavior that may result from climate change. The scenario-based approach allows me to address the deep uncertainty present by quantifying the range of impacts, identifying the most critical parameters, and assessing the sensitivity of local areas to a changing risk. Overall, this body of work quantifies the uncertainties present in several operational and planning decisions for power system applications

Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution