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To examine the applicability of the nacelle transfer function (NTF) derived from nacelle light detection and ranging (LIDAR) measurements to wind turbine power performance testing without a met mast, wind turbine power performance measurement was carried out at the Dongbok wind farm on Jeju Island, South Korea. A nacelle LIDAR was mounted on the na...
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... study was performed at the Dongbok wind farm on Jeju Island, South Korea. The island is located off the southern part of the Korean peninsula, and the Dongbok wind farm is situated on the north-eastern part of the island, as shown in Figure 1. Fifteen 2-MW wind turbines were in operation, and wind turbines no. 1 and 15 were tested in this work. ...
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... calculated measurement sectors were from 223 • to 347 • for wind turbine no. 1 and from 333 • to 97 • for wind turbine no. 15, as shown in Figure 1. The 10-min average wind conditions for one year from 1 January 2017 to 31 December 2017 were measured by the nacelle LIDAR, the met mast, and the nacelle anemometers on the wind turbines, and these were analysed in this work. ...
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... calculated measurement sectors were from 223° to 347° for wind turbine no. 1 and from 333° to 97° for wind turbine no. 15, as shown in Figure 1. The 10-min average wind conditions for one year from 1 Jan 2017 to 31 Dec 2017 were measured by the nacelle LIDAR, the met mast, and the nacelle anemometers on the wind turbines, and these were analysed in this work. ...
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... fi is the relative occurrence of wind speed between Vi − 1 and Vi within bin i. Figure 10 shows the AEP ratio and the uncertainties of AEPNTFL, NL (uAEP, NTF, NL), AEPNTF, Cup (uAEP, NTF, Cup) and AEPCup (uAEP, Cup). A difference of 3.4 % to 7.0 % was found between AEPNTF, NL and AEPCup by means of IEC 61400-12-1. ...
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... f i is the relative occurrence of wind speed between V i − 1 and V i within bin i. Figure 10 shows the AEP ratio and the uncertainties of AEP NTFL, NL (u AEP, NTF, NL ), AEP NTF, Cup (u AEP, NTF, Cup ) and AEP Cup (u AEP, Cup ). A difference of 3.4% to 7.0% was found between AEP NTF, NL and AEP Cup by means of IEC 61400-12-1. ...
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... As per IEC standards, the maximum power difference per bin must be less than 1 % of the bin power or 0.5 % of the rated power of the turbine, and the maximum AEP difference should be less than 1 % of the AEPs at the mean annual wind speeds of 4-11 m/s (Kim et al., 2013). The study conducted by Shin and Ko (2019) compared the deviation in the Annual Energy production of the nacelle performance curve using IEC61400-12-1 and IEC61400-12-2 where the former resulted in a deviation of 3.5-7 %, while the latter showed a deviation of 3.5-8.5 %. The power curve obtained from the raw SCADA data is not accurate because the wind speed measured by the nacelle is lower than the actual wind speed that first hits the wind turbine's rotor (Dai et al., 2021). ...
The power curve of a manufacturer must be compared with the actual power curve after commissioning because various factors lead to deviations. The main purpose of this study was to assess and compare the performance of the N72, N73, and N74 wind turbines of the Adama-II wind farm against the manufacturer's guaranteed power curve. The methods employed were high-correlation-based nacelle anemometry and Artificial Neural Network (ANN). The high-correlation-based nacelle anemometry method used here differs from the conventional IEC61400–12–2 in that it was based on a strong correlation between the mast and nacelle speed of a turbine from which the nacelle transfer function (NTF) was computed. The NTF was obtained by power interpolation from 10-minute wind mast data and the nacelle speed of N72. The measured power curve was then obtained by applying NTF to the nacelle speeds. The turbines were modeled using ANN, illustrating manufacturer's power curve was higher than the modeled values above 9 m/s. According to the analysis, the measured power curves of N73 and N72 were within the manufacturer's AEP uncertainty range at a mean wind speed of 4–9 m/s but showed a deviation of 10–18 % at higher wind speeds. The power production of the N74 turbine was within the manufacturer’s uncertainty limit only at a mean wind speed of 4–7 m/s, whereas at a wind speed above 7 m/s, it deviated by 22–40 %. Therefore, performances of N72 and N73 in terms of the AEP were better, except at wind speeds of 10 and 11 m/s. However, the performance of the N74 turbine was significantly lower than the manufacturer. ANN also gave similar results except at higher wind speeds where high-correlation-based nacelle anemometry performed better. Overall, the proposed methods demonstrated the performance of the turbines compared with the guaranteed power curve.
... Continuous-wave (CW) Doppler lidar can remotely obtain accurate three-dimensional flow observations without disturbing the flow. Consequently, CW lidars are extensively applied to detect wind profiles (Köpp et al., 1984;Peña et al., 2009), assess wind resources (Bingöl et al., 2009;Viselli et al., 2019), test wind turbine performance based on wake measurements (Wagner et al., 2014;Shin and Ko, 2019;Fan et al., 2023), predict the incoming gusts and flow to reduce loads (Bos et al., 2016), and study turbulence around a suspension bridge (Cheynet et al., 2016) and in the near-wake region of a tree (Angelou et al., 2022), with good spatial and temporal resolutions. Recently, two CW lidars were used to measure the two-dimensional downwash wind fields in a horizontal and a vertical plane below a hovering search-andrescue helicopter (Sjöholm et al., 2014). ...
Ultrasonic anemometers mounted on rotary-wing drones have the potential to provide a cost-efficient alternative to the classical meteorological mast-mounted counterpart for atmospheric boundary layer research. However, the propeller-induced flow may degrade the accuracy of free-stream wind velocity measurements by wind sensors mounted on drones – a fact that needs to be investigated for optimal sensor placement. Computational fluid dynamics (CFD) simulations are an alternative to experiments for studying characteristics of the propeller-induced flow but require validation. Therefore, we performed an experiment using three short-range continuous-wave Doppler lidars (light detection and ranging; DTU WindScanners) to measure the complex and turbulent three-dimensional wind field around a hovering drone at low ambient wind speeds. Good agreement is found between experimental results and those obtained using CFD simulations under similar conditions. Both methods conclude that the disturbance zone (defined as a relative deviation from the mean free-stream velocity by more than 1 %) on a horizontal plane located at 1 D (rotor diameter D of 0.71 m) below the drone extends about 2.8 D upstream from the drone center for the horizontal wind velocity and more than 7 D for the vertical wind velocity. By comparing wind velocities along horizontal lines in the upstream direction, we find that the velocity difference between the two methods is ≤ 0.1 ms-1 (less than a 4 % difference relative to the free-stream velocity) in most cases. Both the plane and line scan results validate the reliability of the simulations. Furthermore, simulations of flow patterns in a vertical plane at the ambient speed of 1.3 ms-1 indicate that it is difficult to accurately measure the vertical wind component with less than a 1 % distortion using drone-mounted sonic anemometers.
... The spatial resolution of CW lidars diminishes with the focus distance, but they have higher spatial resolution than pulsed Doppler lidars within several hundred meters. Consequently, CW lidars are extensively applied to detect wind profiles (Köpp et al., 1984;Peña et al., 2009), assess wind resources (Bingöl et al., 2009;Sempreviva et al., 2008;Viselli et al., 2019), test wind turbines' performance based on wake measurements (Wagner et al., 2014;Shin and Ko, 2019;Fan et al., 2023), foresee the incoming gusts and flow to reduce loads (Bos et al., 2016), realize lidar-assisted turbine control to 55 increase power production (Zhang and Yang, 2020;Guo et al., 2022), study turbulence around a suspension bridge (Cheynet , 2016) and in the near-wake region of a tree (Angelou et al., 2022), with good spatial and temporal resolutions. Recently, two CW lidars were applied to measure the two-dimensional downwash wind fields in a horizontal and a vertical plane below a hovering search and rescue helicopter (Sjöholm et al., 2014). ...
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... In this paper, the wind speed of the anemometer tower and the wind speed of the nacelle are divided into 0.5 m/s intervals in this study [41], and the wind speed in front of the nacelle impeller is calculated using the following equation [42]: ...
This paper discusses how the incorporation of high-resolution ground coverage dataset ESA WorldCover into a wind flow field and wake simulation calculation, as well as the use of the coupled wake model for wind farm output simulation, can improve the accuracy of wind resource assessment using engineering examples. In the actual case of grid-connected wind farms in central China, SCADA wind speed data is reconstructed to the free flow wind speed in front of the wind turbine impeller using the transfer function of the nacelle, and the wind farm is modeled using OpenWind software, simulating the wind speed at the height of each wind turbine hub and each wind turbine output. The results show that when other initial data are consistent, using ESA’s high-precision land cover dataset WorldCover 10 m to make roughness lengths which improves the wind farm output simulation accuracy by 8.91%, showing that it is worth trying to apply WorldCover 10 m to the wind farm simulation design. At the same time, this case is used to compare and analyze the application of the Eddy-Viscosity wake model and the two coupled wake models based on the Eddy-Viscosity wake model. The results show that the coupled wake model will have higher accuracy than the Deep Array Eddy Viscosity wake model and it is 1.24% more accurate than the Eddy Viscosity wake model, and the ASM Eddy Viscosity wake model is 5.21% more accurate than the Eddy Viscosity wake model.
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... Since then, several generations have been released, and now the lidars can be precisely focused up to a height of 200 m to reconstruct 3D wind vectors from the conical scanning at different altitudes. Currently, cw lidars are widely applied in wind energy for wind field retrieval [11][12][13], power performance assessment [14,15], wake characterization and modelling [16][17][18], controls and loads [19][20][21], and complex flows [22][23][24] to increase overall annual energy production and reduce shutdown of turbines, which demonstrates that lidars are playing an increasingly important role in wind energy, both in academia and industry. ...
The full-width at half-maximum or probe length of the Lorentzian weighting function of continuous-wave Doppler lidars increases quadratically with the focus distance, which results in a deterioration in the spatial resolution of measurements. What is worse, a Doppler lidar is susceptible to moving objects that are far away from the intended measurement point. Therefore, we suggest a novel configuration to mitigate these problems by deploying two co-planar quarter-wave plates with orthogonal fast axes in the conventional continuous-wave lidar system, without any change to the other optical or electronic components. If the vertically polarized laser beam that we emit goes out and its backscattered beam returns back through the same quarter-wave plate, the returned beam will become horizontally polarized. The horizontally polarized backscattered beam cannot beat with the vertically polarized local oscillator to generate a Doppler signal. However, the polarization of the returned beam will remain unchanged if the emitted beam travels out through one plate and returns through the other. In this way, the influence of a moving backscattering particle far away from the focus point can be reduced. Both theoretical and experimental results show that, in a proper configuration, the probe length of the continuous-wave lidar can be reduced by 10%, compared with that of the conventional lidar. In addition, the fat tails of the Lorentzian weighting function can be suppressed by up to 80% to reduce the return from a cloud, albeit with a large reduction (perhaps 90%) in the signal power. This investigation provides a potential method to increase the spatial resolution of Doppler wind lidars and suppress the low-hanging cloud return.
... is the hub height wind speed of the i-th wind turbine at time t, the nacelle transfer function is usually used to correct the nacelle wind speed to hub height wind speed [18]; t is the wind shear coefficient at time t; i R and i H are the rotor radius and hub height of the i-th wind turbine respectively; ...
... The calculation method of the relative deterioration degree is shown in Formula (17) for the high-superior evaluation indicators, such as the DWEUC and MPC. For the low-superior evaluation indicators, such as the FLC, RILC, PLLC, and OLC, the calculation method of the relative deterioration degree is shown in Formula (18). ...
The accurate evaluation and fair comparison of wind farms power generation performance is of great significance to the technical transformation and operation and maintenance management of wind farms. However, problems exist in the evaluation indicator systems such as confusion, coupling and broadness, and the influence of wind energy resource differences not being able to be effectively eliminated, which makes it difficult to achieve the fair comparison of power generation performance among different wind farms. Thus, the evaluation indicator system and comprehensive evaluation method of wind farm power generation performance, including the influence of wind energy resource differences, are proposed in this paper to address the problems above, to which some new concepts such as resource conditions, ideal performance, reachable performance, actual performance, and performance loss are introduced in the proposed indicator system; the combination of statistical and comparative indicators are adopted to realize the quantitative evaluation, indicator decoupling, fair comparison, and loss attribution of wind farm power generation performance. The proposed comprehensive evaluation method is based on improved CRITIC (Criteria Importance though Intercrieria Correlation) weighting method, in which the uneven situation of different evaluation indicators and the comprehensive comparison of power generation performance among different wind farms shall be overcome and realized. Several sets of data from Chinese wind farms in service are used to validate the effectiveness and applicability of the proposed method by taking the comprehensive evaluation models based on CRITIC weighting method and entropy weighting method as the benchmarks. The results demonstrated that the proposed evaluation indicator system works in the quantitative evaluation and fair comparison of wind farm design, operation, and maintenance and traces the source of power generation performance loss. In addition, the results of the proposed comprehensive evaluation model are more in line with the actual power generation performance of wind farms and can be applied to the comprehensive evaluation and comparison of power generation performance of different wind farms.
... Such values are known to result from power curves measured with shaded anemometer [36]. This observation highlights the need for careful screening for data quality when using power curves from manufacturers and databases. ...
... Such values are known to result from power curves measured with shaded anemometer (Shin and Ko, 2019). This observation highlights the need for careful screening for data quality when using power curves from manufacturers and databases. ...
A wind turbine's power curve relates its power production to the wind speed it experiences. The typical shape of a power curve is well known and has been studied extensively. However, power curves of individual turbine models can vary widely from one another. This is due to both the technical features of the turbine (power density, cut-in and cut-out speeds, limits on rotational speed and aerodynamic efficiency), and environmental factors (turbulence intensity, air density, wind shear and wind veer). Data on individual power curves are often proprietary and only available through commercial databases. We therefore develop an open-source model for pitch regulated horizontal axis wind turbine which can generate the power curve of any turbine, adapted to the specific conditions of any site. This can employ one of six parametric models advanced in the literature, and accounts for the eleven variables mentioned above. The model is described, the impact of each technical and environmental feature is examined, and it is then validated against the manufacturer power curves of 91 turbine models. Versions of the model are made available in MATLAB, R and Python code for the community.
... Alternatively, a number of studies have investigated the implementation of ground-based Doppler light detection and ranging systems (LIDARs) for characterization of wind turbine inflow conditions [22][23][24], whereas a more recent approach is to place the LIDARs on the wind turbine itself, in order to measure the incoming wind speed more accurately while the wake of the turbine can also be characterized [25][26][27]. A promising new approach to address this issue is using large-scale particle image velocimetry (PIV) to measure velocity profiles at the inflow and the wake of the turbine [28,29], which is still in development. ...
Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of wind turbines that can be used for power prediction. The proposed model is tested and compared to traditional power curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and turbulence measurements from a nearby meteorological tower are used in the models. The results for a period of four months showed an improvement of 51% in power prediction accuracy, compared to the standard power curve.
