scholarly journals Offshore wind farm global blockage measured with scanning lidar

2021 ◽  
Vol 6 (2) ◽  
pp. 521-538
Author(s):  
Jörge Schneemann ◽  
Frauke Theuer ◽  
Andreas Rott ◽  
Martin Dörenkämper ◽  
Martin Kühn
Keyword(s):  
Wind Speed ◽  
Long Range ◽  
Wind Farm ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Stability Estimates ◽  
Offshore Wind Farm ◽  
Scanning Lidar ◽  
High Thrust ◽  
Blockage Effect

Abstract. The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global-blockage effect, in relation to atmospheric-stability estimates and wind farm operational states. We measured the inflow of a 400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced, and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global-blockage effect, which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a 4 % decrease in wind speed (accuracy range of 2 % to 6 %) at transition piece height (24.6 m) upwind of the wind farm with the turbines operating at high thrust coefficients above 0.8 in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions and for situations with low thrust coefficients (i.e. approx. 0 for not operating turbines and ≤ 0.3 for turbines operating far above rated wind speed) we identified no wind speed deficit. We discussed the significance of our measurements and possible sources of error in long-range scanning lidar campaigns and give recommendations on how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We further conclude that global blockage is dependent on atmospheric stratification.

scholarly journals Offshore wind farm global blockage measured with scanning lidar

2020 ◽  
Author(s):  
Jörge Schneemann ◽  
Frauke Theuer ◽  
Andreas Rott ◽  
Martin Dörenkämper ◽  
Martin Kühn
Keyword(s):  
Wind Speed ◽  
Long Range ◽  
Wind Farm ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Stability Estimates ◽  
Offshore Wind Farm ◽  
Scanning Lidar ◽  
High Thrust ◽  
Blockage Effect

Abstract. The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global blockage effect, in relation to atmospheric stability estimates and wind farm operational states. We measured the inflow of a 400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global blockage effect which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a significant decrease in wind speed at platform height in front of the wind farm of 4.5 % within an accuracy range between 2.5 % and 6.5 % with the turbines operating at high thrust coefficients in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions we identified no wind speed deficit. We discussed the significance of our measurements, possible sources of error in long-range scanning lidar campaigns and give recommendations how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We conclude that global blockage is dependant on atmospheric stratification.

scholarly journals Alignment of scanning lidars in offshore wind farms

2021 ◽  
Author(s):  
Andreas Rott ◽  
Jörge Schneemann ◽  
Frauke Theuer ◽  
Juan José Trujillo Quintero ◽  
Martin Kühn
Keyword(s):  
Wind Turbine ◽  
Long Range ◽  
Wind Farms ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Alignment Error ◽  
Position Information ◽  
Measurement Campaign ◽  
Scanning Lidar ◽  
Range Scanning

Abstract. Long-range Doppler wind lidars are applied more and more for high resolution areal measurements in and around wind farms. Proper alignment, or at least knowledge on how the systems are aligned, is of great relevance here. The paper describes in detail two methods that allow a very accurate alignment of a long-range scanning lidar without the use of extra equipment or sensors. The well-known so-called Hard Targeting allows a very precise positioning and north alignment of the lidar using the known positions of the surrounding obstacles, e.g. wind turbine towers. Considering multiple hard targets instead of only one with a given position in an optimization algorithm allows to increase the position information of the lidar device and minimizes the consequences of using erroneous input data. The method, referred to as Sea Surface Leveling, determines the leveling of the device during offshore campaigns in terms of roll and pitch angle based on distance measurements to the water surface. This is particularly well suited during the installation of the systems to minimize alignment error from the start, but it can also be used remotely during the measurement campaign for verification purposes. We applied and validated these methods to data of an offshore measurement campaign, where a commercial long-range scanning lidar was installed on the transition piece platform of a wind turbine. In addition, we present a model that estimates the quasi-static inclination of the device due to the thrust loading of the wind turbine at different operating conditions. The results show reliable outcomes with a very high accuracy in the range of 0.02° in determining the leveling. The importance of the exact alignment as well as the possible applications are discussed in this paper. In conclusion, these methods are useful tools that can be applied without extra effort and contribute significantly to the quality of successful measurement campaigns.

scholarly journals Validation of Numerical Models of the Offshore Wind Turbine From the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Wojciech Popko ◽  
Amy Robertson ◽  
Jason Jonkman ◽  
Fabian Wendt ◽  
Philipp Thomas ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Phase Iii ◽  
Offshore Wind Turbine ◽  
Discrete Fourier Transforms ◽  
Simulation Tools ◽  
Axial Forces

Abstract The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on a Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) idling below the cut-in wind speed, (2) rotor-nacelle assembly (RNA) rotation maneuver below the cut-in wind speed, (3) power production below and above the rated wind speed, and (4) shutdown. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) strains and accelerations recorded on the support structure and (2) pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools; therefore, strains were calculated based on out-of-plane bending moments, axial forces, and cross-sectional properties of the structural members. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms, power spectral densities, and probability density functions of strains and accelerometers. A good match was achieved between the measurements and models setup by OC5 Phase III participants.

scholarly journals Recovery processes in a large offshore wind farm

2021 ◽  
Vol 6 (5) ◽  
pp. 1089-1106
Author(s):  
Tanvi Gupta ◽  
Somnath Baidya Roy
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
High Wind ◽  
Offshore Wind Farm ◽  
High Wind Speed ◽  
Recovery Processes ◽  
Speed Range

Abstract. Wind turbines in a wind farm extract energy from the atmospheric flow and convert it into electricity, resulting in a localized momentum deficit in the wake that reduces energy availability for downwind turbines. Atmospheric momentum convergence from above, below, and the sides into the wakes replenishes the lost momentum, at least partially, so that turbines deep inside a wind farm can continue to function. In this study, we explore recovery processes in a hypothetical offshore wind farm with particular emphasis on comparing the spatial patterns and magnitudes of horizontal- and vertical-recovery processes and understanding the role of mesoscale processes in momentum recovery in wind farms. For this purpose, we use the Weather Research and Forecasting (WRF) model, a state-of-the-art mesoscale model equipped with a wind turbine parameterization, to simulate a hypothetical large offshore wind farm with different wind turbine spacings under realistic initial and boundary conditions. Different inter-turbine spacings range from a densely packed wind farm (case I: low inter-turbine distance of 0.5 km ∼ 5 rotor diameter) to a sparsely packed wind farm (case III: high inter-turbine distance of 2 km ∼ 20 rotor diameter). In this study, apart from the inter-turbine spacings, we also explored the role of different ranges of background wind speeds over which the wind turbines operate, ranging from a low wind speed range of 3–11.75 m s−1 (case A) to a high wind speed range of 11–18 m s−1 (case C). Results show that vertical turbulent transport of momentum from aloft is the main contributor to recovery in wind farms except in cases with high-wind-speed range and sparsely packed wind farms, where horizontal advective momentum transport can also contribute equally. Vertical recovery shows a systematic dependence on wind speed and wind farm density that is quantified using low-order empirical equations. Wind farms significantly alter the mesoscale flow patterns, especially for densely packed wind farms under high-wind-speed conditions. In these cases, the mesoscale circulations created by the wind farms can transport high-momentum air from aloft into the atmospheric boundary layer (ABL) and thus aid in recovery in wind farms. To the best of our knowledge, this is one of the first studies to look at wind farm replenishment processes under realistic meteorological conditions including the role of mesoscale processes. Overall, this study advances our understanding of recovery processes in wind farms and wind farm–ABL interactions.

scholarly journals Analysing uncertainties in offshore wind farm power output using measure–correlate–predict methodologies

2020 ◽  
Vol 5 (2) ◽  
pp. 601-621
Author(s):  
Michael Denis Mifsud ◽  
Tonio Sant ◽  
Robert Nicholas Farrugia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Power Output ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Offshore Wind ◽  
Energy Yield ◽  
Offshore Wind Farm ◽  
Power And Energy

Abstract. This paper investigates the uncertainties resulting from different measure–correlate–predict (MCP) methods to project the power and energy yield from a wind farm. The analysis is based on a case study that utilises short-term data acquired from a lidar wind measurement system deployed at a coastal site in the northern part of the island of Malta and long-term measurements from the island's international airport. The wind speed at the candidate site is measured by means of a lidar system. The predicted power output for a hypothetical offshore wind farm from the various MCP methodologies is compared to the actual power output obtained directly from the input of lidar data to establish which MCP methodology best predicts the power generated. The power output from the wind farm is predicted by inputting wind speed and direction derived from the different MCP methods into windPRO® (https://www.emd.dk/windpro, last access: 8 May 2020). The predicted power is compared to the power output generated from the actual wind and direction data by using the normalised mean absolute error (NMAE) and the normalised mean-squared error (NMSE). This methodology will establish which combination of MCP methodology and wind farm configuration will have the least prediction error. The best MCP methodology which combines prediction of wind speed and wind direction, together with the topology of the wind farm, is that using multiple linear regression (MLR). However, the study concludes that the other MCP methodologies cannot be discarded as it is always best to compare different combinations of MCP methodologies for wind speed and wind direction, together with different wake models and wind farm topologies.

scholarly journals An equivalent modeling method for Offshore Wind Farms based on fault characteristics analysis

2019 ◽  
Vol 107 ◽  
pp. 01004
Author(s):  
Haiyan Tang ◽  
Guanglei Li ◽  
Linan Qu ◽  
Yan Li
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Modeling Method ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Equivalent Model ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Characteristics Analysis

A large offshore wind farm usually consists of dozens or even hundreds of wind turbines. Due to the limitation of the simulation scale, it is necessary to develop an equivalent model of offshore wind farms for power system studies. At present, the aggregation method is widely adopted for wind farm equivalent modeling. In this paper, the topology, electrical parameters, operating conditions and individual turbine characteristics of the offshore wind farms are taken into consideration. Firstly, the output power distribution of offshore wind farm, the voltage distribution of the collector system and the fault ride-through characteristics of wind turbines are analyzed. Then, a dynamic equivalent modeling method for offshore wind farms is developed based on the fault characteristics analysis. Finally, the proposed method is validated through time-domain simulation.

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

2021 ◽  
Vol 236 ◽  
pp. 114002
Author(s):  
Mehdi Neshat ◽  
Meysam Majidi Nezhad ◽  
Ehsan Abbasnejad ◽  
Seyedali Mirjalili ◽  
Lina Bertling Tjernberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Farm ◽  
Evolutionary Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting
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