Nacelle Anemometer Research Articles

Evaluation and Long-Term Prediction of Annual Wind Farm Energy Production

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures.

A General Method For The Diagnosis Of Wind Turbine Systematic Yaw Error Based Solely On SCADA Data

Accurate diagnosis of systematic errors affecting wind turbine operation is fundamental to maximize the energy capture. Based on this, this work deals with the systematic yaw error, occurring when the wind vane sensor is incorrectly aligned with the rotor shaft. The objective is formulating a method for individuating the presence and estimating the amount of systematic yaw error, based solely on Supervisory Control And Data Acquisition (SCADA) data analysis. The state of the art is based on inferring the presence of the static yaw error by detecting an under-performance. Therefore, there is a gap as regards the estimation of how much the static yaw error really is. The proposed method introduces major methodological novelties for tackling such issue. Indeed, nacelle wind speed measurements are used in this work because the presence of the systematic yaw error has a detectable effect on them and they are not influenced by the control system. Furthermore, this work is the first in the literature employing measurements collected with wind turbine stopped, which allows circumventing the presence of the assembly angle induced by the rotor rotation. The proposed method is based on the idea that, if two nacelle anemometers are present, the ratio between the two wind speed measurements should change in presence of a static yaw error. Through a test case discussion, it is shown that with the proposed method it is possible to estimate the static yaw error as reliably as with LiDAR measurements.

Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades

Leading edge erosion of wind turbine blades is a major contributor to wind farm energy yield losses and maintenance costs. Presented is a multidisciplinary framework for predicting rain erosion lifetimes of wind turbine blades. Key aim is assessing the sensitivity of lifetime predictions to: modeling aspects (material erosion model, blade aerodynamics), input data and/or their preprocessing (joint frequency distribution of wind speed and droplet size based on synchronous site-specific measurements versus frequency distribution generated with partly site-agnostic modeling standards, wind speed records of nacelle anemometer or extrapolated at hub height from met masts), and environmental conditions (UV weathering). The analyses consider a Northwest England onshore site where a utility-scale turbine is operational, focus on a reference 5 MW turbine assumed operational at the site, and use a typical leading edge coating material. It is found that the largest variations in erosion lifetime predictions are due to material erosion model (based on rain erosion test data or fundamental material properties) and wind and rain model (measurement-based joint wind speed and droplet size distribution or standard-based modeled distribution). The use of joint wind and rain distribution also enables identifying wind/rain states with highest erosion potential, knowledge paramount to deploying erosion-safe turbine control.

Diagnosis of wind turbine systematic yaw error through nacelle anemometer measurement analysis

The power produced by a wind turbine can be considerably affected by the presence of systematic errors, which are particularly difficult to diagnose. This study deals with wind turbine systematic yaw error and proposes a novel point of view for diagnosing and quantifying its impact on the performance. The keystone is that, up to now in the literature, the effect of the yaw error on the nacelle wind speed measurements of the affected wind turbine has been disregarded. Given this, in this work a new method based on the general principle of flow equilibrium is proposed for the diagnosis of such type of error. It is based on recognizing that a misaligned wind turbine measures the wind speed differently with respect to when it is aligned. The method is shown to be effective for the diagnosis of two test cases, about which an independent estimate of the yaw error is available from upwind measurements (spinner anemometer). A data-driven generalization of the concept of relative performance is then formulated and employed for estimating how much the systematic yaw error affects wind turbine performance. It is shown that the proposed method is more appropriate than methods employing wind speed measurements (like the power curve), which are biased by the presence of the error. The results of this study support that SCADA-collected data can be very useful to diagnose wind turbine systematic yaw error, provided that a critical analysis about their use is done.

Microscale simulations of extreme events in complex terrain driven by mesoscalar budget components

In this work, we apply a downscaling strategy to analyze extreme weather events that may impact wind farm operation. The coupling applies mesoscale momentum budget components (tendencies) from the WRF model as forcing terms to the governing microscale equations. Our study focuses on flow over complex terrain during specific days to reproduce extreme weather events that produced wind turbine damage. The interaction of the meso- and micro-scale features are relevant in the simulation of extreme conditions. The simulation results are compared with observations from nacelle anemometers of the wind turbines in two different wind farms by analyzing time series and wind profiles.The microscale code Alya, developed at the Barcelona Supercomputing Center (BSC), is closed with URANS and LES closures to solve the momentum and energy equations. Both closures use the same mesoscalar to microscalar coupling methodology and are used in this work to simulate the wind flow. We present the implementation of the mesoscalar coupling to the microscale solver when using URANS and LES closures.We show that the coupling via tendencies has excellent potential for understanding transient events under extreme weather conditions in very complex terrain. The wind industry can use such simulations to enhance forensic analysis in cases of wind turbine accidents or any other event that may impact turbine operation, such as high turbulence phenomena. We test the ability of the meso- to microscale coupling model to reproduce extreme events with regard to quantities of interest in wind energy.Simulation results using URANS and LES closures agree reasonably well with observations. In some scenarios, the LES provides results that are closer to measurements. LES models have the advantage of providing wind gusts. We compare the accuracy and performance (CPU-time) of the URANS vs. LES approaches.

Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

Wind Turbine Operation Curves Modelling Techniques

Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity.

Numerical Investigation of Three-Dimensional and Vortical Flow Phenomena to Enhance the Power Performance of a Wind Turbine Blade

The performance of a wind turbine generator (WTG) is highly dependent on the interaction of a rotor blade with complex fluid behaviors, especially the induced vortex structure. In this paper, vortical flows around a blade were first investigated by the unsteady Reynolds averaged Navier–Stokes (RANS) simulation with shear stress transport (SST) turbulence model. It showed that the vortical flows were strongly formed at the blade tip due to the 3D behavior of the boundary layers dominated by pressure gradient. The strong secondary flow was also formed at the near hub due to the Coriolis force and the centrifugal force. At the interacting region of the rotating blade with the tower, the power production was reduced by 22.1% due to the high-pressure fluctuation of the 3P frequency. Based on the close investigation, methods for enhancing the power performance of a WTG were explored, which included the optimization of winglet and ogee design for the blade tip and optimal layout of the nacelle anemometer. The optimized winglet achieved the increase of aerodynamic performance with 0.54%, and the optimal location of the nacelle anemometer was found with a low-turbulence intensity level of 0.003 normalized by the rotor tip speed. The results showed that the traditional anemometer needs to consider the intrinsic flow angle of 11.43° to avoid the loss of aerodynamic performance caused from yaw error.

Wind Turbine Multivariate Power Modeling Techniques for Control and Monitoring Purposes

Abstract Wind turbine performance monitoring is a complex task because the power has a multivariate dependence on ambient conditions and working parameters. Furthermore, wind turbine nacelle anemometers are placed behind the rotor span and the control system estimates the upwind flow through a nacelle transfer function: this introduces a data quality issue. This study is devoted to the analysis of data-driven techniques for wind turbine performance control and monitoring: operation data of six 850 kW wind turbines sited in Italy have been employed. The objective of this study is an assessment of several easily implementable techniques and input variables selections for data-driven models whose target is the power of a wind turbine. Three model types are selected: one is linear (Principal Component Regression) and two are nonlinear (Support Vector Regression with Gaussian Kernel and Feedforward Artificial Neural Network). The models' validation provides meaningful indications: the linear model in general has lower performance because it cannot reproduce properly the nonlinear pitch behavior when approaching rated power. Therefore, it is concluded that a nonlinear model should be employed and the achieved mean absolute error is of the order of 1.3% of the rated power. Furthermore, the errors are kept at the order of 2% of the rated power for the models whose input is the rotor speed instead that wind speed: this observation supports that, in case it is needed because of nacelle anemometer biases, the power monitoring can be acceptably implemented using the rotor speed.

How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, also known as shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer), and rotor-equivalent wind speed (REWS; a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured by either the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW or up to 5 % of the rated power for a single 1.5 MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

Plasma actuation for leading edge separation control on 300-kW rotor blades with chord length around 1 m at a Reynolds number around 1.6 × 106

The second field test of a plasma-assisted 300-kW wind turbine was performed to investigate the flow-control authority of plasma-actuation technology at Reynolds numbers (Re) exceeding 106. The turbine rotational speed was regulated via pitch activation in the power-saving operation mode to limit Re to approximately 106. Additional generator-torque control was adopted to make the angles of attack exceed those allowable via original control. The leading-edge plasma electrode was activated at 10-min intervals and turbine characteristics with and without plasma actuation were compared using 1-min-averaged SCADA data, including wind conditions measured by a nacelle anemometer, obtained during a 6-day test. Results of this study reveal that the tip speed ratio (TSR) reduces under high-wind-speed conditions owing to additional torque control. Further, the power coefficient drops in the low TSR operating range, and improves upon plasma actuation. This has been statistically proven for at least one TSR bin. This result implies the existence of leading-edge separation under the additional torque control, and that plasma-actuation technology can effectively control the flow in the 1.6–1.8 × 106 Re range. Results of this study demonstrate the potential of and need for further investigation of the proposed technology to facilitate its application for multi-megawatt wind-turbine.

Impact of field roughness and power losses, turbulence intensity on electricity production for an onshore wind farm

<span lang="EN-US">When designing a power generation project from a different source, and in our case study, wind, when calculating the annual energy produced, it is necessary to define and calculate the losses incurred in the system. The main cause of losses in a wind park is due to the oscillations caused by the turbulence of the air around the turbine because of roughness of terrain. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. These analyses are very important for understanding the fatigue and mechanical stress on the wind turbines. Then significance of the site ruggedness index (RIX) and the associated performance indicator (ΔRIX) are confirmed for terrain and the consequences of applying WAsP outside its operating envelope are quantified.</span>

Integration of System Level CFD Simulations into the Development Process of Wind Turbine Prototypes

During the development process of wind turbines, there is a rising demand for detailed flow and multi-physics analyses. Especially the system level computational fluid dynamics (CFD), focusing not on a single component or aspect, but on the turbine as a system of geometric and dynamic properties can be used to improve the turbine quality and time to market. In this paper the integration of the system level CFD into the development process of Enercon wind turbines is introduced. Further two example applications are detailed presenting the benefits of CFD based nacelle anemometer calibration and a risk assessment study based on aero-servo-elastic CFD simulations.

Data-Dirven Method for Wake Effect Analysis on Nacelle Anemometer

For most wind turbines, blade wakes affect the measurement of nacelle anemometer, result in the inconsistency between nacelle wind speed (NWS) and free stream wind speed, which seriously affects the power forecasting and performance evaluation of wind turbine. This paper proposes a data-driven method to analyse the wake effect on nacelle anemometer. At first, we use Relevance Vector Machine to establish a site calibration model between Lidar wind speed (LWS) and NWS. After that, we can use the calibrated LWS to replace the free stream wind speed, and wake effect on nacelle anemometer can be evaluated by comparing the calibrated LWS and NWS. Then, Wind Turbine Power Curve (WTPC) is applied to make a detail analysis of wake effect on nacelle anemometer. The experimental results show that wake effect accelerates the air velocity behind the impeller. Therefore, WTPC fitted by NWS is “lower” than the real one. However, SCADA system overcorrects the wake effect, thus WTPC fitted by SCADA data is “higher” than the real power curve.

The variability of wind resources in complex terrain and its relationship with atmospheric stability

One of the greatest challenges of modern wind energy research is to improve our understanding of and ability to model wind farm flows, especially those over complex terrain. In this endeavor, multi-disciplinary field experiments have revealed, with unprecedented detail, the intricate flow patterns formed as winds interact with topography, and these are strongly interconnected with atmospheric stability. Therefore, we investigated how atmospheric stability affects the spatial variability of wind resources across a site set in complex terrain. A dataset obtained from a recent measurement campaign, involving the use of 6 masts and 90 turbine nacelle cup anemometers, as well as processed data on atmospheric stability obtained from a sonic three-component anemometer were used. The results revealed that at night, with low-turbulence and stable conditions, the spatial variability in wind resources can be twice as large as that for daytime, high-turbulence and unstable conditions. Wind resources at 90 locations were ranked from best (R = 1) to worst (R = 90) based on nacelle anemometers. Changes of up to 61 positions occurred at specific locations, depending on conditions associated with atmospheric stability. Therefore, these outcomes raise important questions regarding the current best practices of micro-siting and the capability of wind farm design models.

Nacelle anemometer measurement‐based extremum‐seeking wind turbine region‐2 control for improved convergence in fluctuating wind

AbstractFor region‐2 operation of wind turbines in practice, the optimal torque gain can deviate from the nominal value because of the variations in turbine and wind conditions. The extremum‐seeking control (ESC) has shown its potential as a model‐free region‐2 control solution in some recent work; however, the ESC with rotor power feedback suffers from undesirable convergence under fluctuating wind. In this paper, we propose to use an estimated power coefficient as the objective function for the torque‐gain ESC, where the hub‐height free‐stream wind speed (FSWS) is estimated with the nacelle anemometer measurement on the basis of the so‐called nacelle transfer function (NTF) between the nacelle anemometer and met‐tower measurement. A sensitivity analysis is performed to quantify the impact of the wind speed estimation error on the estimation of power coefficient. An ESC integrated interregion switching scheme is proposed to avoid the load increase. Simulation results show that, compared with the power feedback‐based ESC, the proposed method can greatly improve the convergence rate of ESC under fluctuating wind, even under relatively large wind speed estimation error. Evaluation for the fatigue loads of wind turbine shows that the proposed control strategy induces mild increase of the wind turbine load.

An Operation Data-Based Method for the Diagnosis of Zero-Point Shift of Wind Turbines Yaw Angle

Abstract The alignment of the wind turbine yaw to the wind direction is an important topic for wind turbine technology by several points of view. For example, the negative impact on power production of an undesired non-optimal yaw alignment can be impressive. The diagnosis of zero-point shifting of the yaw angle is commonly performed by adopting supplementary measurement sources, as for example, light detection and ranging (LIDAR) anemometers. The drawback is that these measurement campaigns have a certain cost against an uncertain diagnosis outcome. There is therefore an increasing interest from wind turbine practitioners in the formulation of zero-point yaw angle shift diagnosis techniques through the use of nacelle anemometer data. This work is devoted to this task and is organized as a test case discussion: a wind farm featuring six multi-megawatt wind turbines is considered. The study of the power factor Cp as function of the yaw error (estimated through nacelle anemometer data) is addressed. The proposed method has been validated through the detection of a 8 deg zero-point shift of the yaw angle of one wind turbine in the test case wind farm. After the correction of this offset, the performance of the wind turbine of interest is shown to be comparable with the nominal. The results of this work therefore support that an appropriate analysis of nacelle anemometer and operation data can be effective for the diagnosis of zero-point shift of the yaw angle of wind turbines.

Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure, the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at the Sole du Moulin Vieux (SMV) onshore wind farm in France and the Horns Rev-I offshore wind farm in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15 % to 20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are on the order of 2.5 % for a two-wind-turbine case with close spacing and 1 % to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in the scope of the national project SMARTEOLE.

Data assimilation impact of in situ and remote sensing meteorological observations on wind power forecasts during the first Wind Forecast Improvement Project (WFIP)

AbstractDuring the first Wind Forecast Improvement Project (WFIP), new meteorological observations were collected from a large suite of instruments, including wind velocities measured on networks of tall towers provided by wind industry partners, wind speeds measured by cup anemometers mounted on the nacelles of wind turbines, and wind profiles by networks of Doppler sodars and radar wind profilers. Previous data denial studies found a significant improvement of up to 6% root mean squared error (RMSE) reduction for short‐term wind power forecasts due to the assimilation of all of these observations into the National Oceanic and Atmospheric Administration (NOAA) Rapid Refresh (RAP) forecast model using a 3D variational data assimilation scheme. As a follow‐on study, we now investigate the impacts of assimilating into the RAP model either the additional remote sensing observations (sodars and radar wind profilers) alone or assimilating the industry‐provided in situ observations (tall towers and nacelle anemometers) alone, in addition to routinely available standard meteorological data sets. The more numerous tall tower/nacelle observations provide a relatively large improvement through the first 3 to 4 hours of the forecasts, which diminishes to a negligible impact by forecast hour 6. In comparison, the sparser vertical profiling sodars/radars provide an initially smaller impact that decays at a much slower rate, with a positive impact present through the first 12 hours of the forecast. Large positive assimilation impacts for both sets of instruments are found during daytime hours, while small or even negative impacts are found during nighttime hours.

Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement

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 nacelle of a 2-MW wind turbine to measure wind conditions in front of the turbine rotor, and an 80-m-high met mast was installed near another wind turbine to measure the free-stream wind speed. The power measurement instruments were installed in the turbine tower base, and wind speeds measured by the nacelle anemometer of the turbine were collected by the SCADA (Supervisory control and data acquisition) system. The NTF was determined by the table method, and then the power curve drawn using the NTF by the nacelle LIDAR (PCNTF, NL) was compared with the power curves drawn in compliance with International Electrotechnical Commission (IEC) standards, 61400-12-1 and 61400-12-2. Next, the combined standard uncertainties of the power curves were calculated to clarify the magnitude of the components of the uncertainties. The uncertainties of annual energy production (AEP) were also estimated by assuming that wind speed is a Rayleigh cumulative distribution. As a result, the PCNTF, NL was in good agreement with the power curves drawn in accordance with the IEC standards. The combined standard uncertainty of PCNTF, NL was almost the same as that of the power curve based on IEC 61400-12-2.