scholarly journals Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2351 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Matteo Becchetti ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Data Analysis ◽  
Wind Turbine ◽  
Error Detection ◽  
Wind Farm ◽  
Assessment Method ◽  
Test Case ◽  
Performance Impact ◽  
Use Of Data ◽  
Yaw Angle ◽  
Multivariate Kernel Regression

The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4 ∘ affects the performance up to the order of the 1.5% of the Annual Energy Production.

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

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Ludovico Terzi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Negative Impact ◽  
Zero Point ◽  
Test Case ◽  
Case Discussion ◽  
Angle Shift ◽  
Points Of View ◽  
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.

scholarly journals Improving the FLORIS wind plant model for compatibility with gradient-based optimization

2017 ◽  
Vol 41 (5) ◽  
pp. 313-329 ◽  
Author(s):  
Jared J Thomas ◽  
Pieter MO Gebraad ◽  
Andrew Ning
Keyword(s):  
Steady State ◽  
Wind Turbine ◽  
Case Studies ◽  
Wind Farm ◽  
Optimization Problems ◽  
Algorithmic Differentiation ◽  
Function Calls ◽  
Gradient Based ◽  
Wake Model ◽  
Yaw Angle

The FLORIS (FLOw Redirection and Induction in Steady-state) model, a parametric wind turbine wake model that predicts steady-state wake characteristics based on wind turbine position and yaw angle, was developed for optimization of control settings and turbine locations. This article provides details on changes made to the FLORIS model to make the model more suitable for gradient-based optimization. Changes to the FLORIS model were made to remove discontinuities and add curvature to regions of non-physical zero gradient. Exact gradients for the FLORIS model were obtained using algorithmic differentiation. A set of three case studies demonstrate that using exact gradients with gradient-based optimization reduces the number of function calls by several orders of magnitude. The case studies also show that adding curvature improves convergence behavior, allowing gradient-based optimization algorithms used with the FLORIS model to more reliably find better solutions to wind farm optimization problems.

scholarly journals A Study of the Impact of Pitch Misalignment on Wind Turbine Performance

Machines ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 8 ◽  
Author(s):  
Davide Astolfi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Reactive Power ◽  
Power Production ◽  
Test Case ◽  
Before And After ◽  
Input Variables ◽  
The Impact

Pitch angle control is the most common means of adjusting the torque of wind turbines. The verification of its correct function and the optimization of its control are therefore very important for improving the efficiency of wind kinetic energy conversion. On these grounds, this work is devoted to studying the impact of pitch misalignment on wind turbine power production. A test case wind farm sited onshore, featuring five multi-megawatt wind turbines, was studied. On one wind turbine on the farm, a maximum pitch imbalance between the blades of 4.5 ° was detected; therefore, there was an intervention for recalibration. Operational data were available for assessing production improvement after the intervention. Due to the non-stationary conditions to which wind turbines are subjected, this is generally a non-trivial problem. In this work, a general method was formulated for studying this kind of problem: it is based on the study, before and after the upgrade, of the residuals between the measured power output and a reliable model of the power output itself. A careful formulation of the model is therefore crucial: in this work, an automatic feature selection algorithm based on stepwise multivariate regression was adopted, and it allows identification of the most meaningful input variables for a multivariate linear model whose target is the power of the wind turbine whose pitch has been recalibrated. This method can be useful, in general, for the study of wind turbine power upgrades, which have been recently spreading in the wind energy industry, and for the monitoring of wind turbine performances. For the test case of interest, the power of the recalibrated wind turbine is modeled as a linear function of the active and reactive power of the nearby wind turbines, and it is estimated that, after the intervention, the pitch recalibration provided a 5.5% improvement in the power production below rated power. Wind turbine practitioners, in general, should pay considerable attention to the pitch imbalance, because it increases loads and affects the residue lifetime; in particular, the results of this study indicate that severe pitch misalignment can heavily impact power production.

Precision Computation of Wind Turbine Power Upgrades: An Aerodynamic and Control Optimization Test Case

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Mario Luca Fravolini ◽  
Silvia Cascianelli ◽  
Ludovico Terzi
Keyword(s):  
Wind Turbine ◽  
Power Output ◽  
Wind Farm ◽  
Residual Life ◽  
Test Case ◽  
Labor Costs ◽  
Passive Flow Control ◽  
Production Improvement ◽  
Input Variables ◽  
And Control

Wind turbine upgrades have recently been spreading in the wind energy industry for optimizing the efficiency of the wind kinetic energy conversion. These interventions have material and labor costs; therefore, it is fundamental to estimate the production improvement realistically. Furthermore, the retrofitting of the wind turbines sited in complex environments might exacerbate the stress conditions to which those are subjected and consequently might affect the residual life. In this work, a two-step upgrade on a multimegawatt wind turbine is considered from a wind farm sited in complex terrain. First, vortex generators and passive flow control devices have been installed. Second, the management of the revolutions per minute has been optimized. In this work, a general method is formulated for assessing the wind turbine power upgrades using operational data. The method is based on the study of the residuals between the measured power output and a judicious model of the power output itself, before and after the upgrade. Therefore, properly selecting the model is fundamental. For this reason, an automatic feature selection algorithm is adopted, based on the stepwise multivariate regression. This allows identifying the most meaningful input variables for a multivariate linear model whose target is the power of the upgraded wind turbine. For the test case of interest, the adopted upgrade is estimated to increase the annual energy production to 2.6 ± 0.1%. The aerodynamic and control upgrades are estimated to be 1.8% and 0.8%, respectively, of the production improvement.

scholarly journals Three-Stage Wind Turbine Assessment Method: Condition Monitoring, Failure Prediction, And Health Assessment

2021 ◽  
Author(s):  
Yongsheng Qi ◽  
Tongmei Jing ◽  
Chao Ren ◽  
Xuejin Gao
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Farm ◽  
Health Management ◽  
Health Assessment ◽  
Failure Prediction ◽  
Fuzzy Comprehensive Evaluation ◽  
Assessment Method ◽  
Generalized Regression Neural Network ◽  
Monitoring Method

Abstract To improve the wind turbine shutdown early warning ability, we present a generalized model for wind turbine (WT) prognosis and health management (PHM) based on the data collected from the SCADA system. First, a new condition monitoring method based on kernel entropy component analysis (KECA) was developed for nonlinear data. Then, an aggregate statistic T was designed to express the state change of the monitoring parameters. As the features were submerged because of the diversity and nonlinearity of SCADA data, an enhanced generalized regression neural network (GRNN) method—KECA-GRNN—for failure prediction was developed by adding KECA for feature extraction to improve the predictive performance. Finally, the results of the KECA-GRNN model were visualized by a bubble chart, which made the health assessment results of the WT more intuitive. Similarly, the fusion residual was defined to analyze the health trend of the WT, and the health status of the WT was represented by two visualization methods—bubble chart and fuzzy comprehensive evaluation. Furthermore, they were evaluated using SCADA data that were collected from a wind farm. Observations from the results of the model indicated the ability of the approach to trend and assess turbine degradation before known downtime occurrences.

Analysis of Wind Turbine Wakes Through Time-Resolved and SCADA Data of an Onshore Wind Farm

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Francesco Castellani ◽  
Paolo Sdringola ◽  
Davide Astolfi
Keyword(s):  
Wind Turbine ◽  
Complex Terrain ◽  
Spectral Properties ◽  
Wind Farm ◽  
Point Of View ◽  
Test Case ◽  
Time Resolved ◽  
Onshore Wind ◽  
Yaw Control ◽  
Different Levels

An experimental study is conducted on wind turbine wakes and their effects on wind turbine performances and operation. The test case is a wind farm located on a moderately complex terrain, featuring four turbines with 2 MW of rated power each. Two interturbine distances characterize the layout: 4 and 7.5 rotor diameters. Therefore, it is possible to study different levels of wake recovery. The processed data are twofold: time-resolved series, whose frequency is in the order of the hertz, and supervisory control and data acquisition (SCADA) data with 10 min of sampling time. The wake fluctuations are investigated adopting a “slow” point of view (SCADA), on a catalog of wake events spanned over a long period, and a “fast” point of view of selected time-resolved series of wake events. The power ratios between downstream and upstream wind turbines show that the time-resolved data are characterized by a wider range of fluctuations with respect to the SCADA. Moreover, spectral properties are assessed on the basis of time-resolved data. The combination of meandering wind and yaw control is observed to be associated with different spectral properties depending on the level of wake recovery.

scholarly journals Measurement procedures for characterization of wind turbine wakes with scanning Doppler wind LiDARs

2013 ◽  
Vol 10 (1) ◽  
pp. 71-75 ◽  
Author(s):  
G. V. Iungo ◽  
F. Porté-Agel
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Wake Flow ◽  
Lidar Measurement ◽  
Wake Turbulence ◽  
Main Challenge ◽  
Measuring Techniques ◽  
The Mean ◽  
Yaw Angle

Abstract. The wake flow produced from an Enercon E-70 wind turbine is investigated through three scanning Doppler wind LiDARs. One LiDAR is deployed upwind to characterize the incoming wind, while the other two LiDARs are located downstream to carry out wake measurements. The main challenge in performing measurements of wind turbine wakes is represented by the varying wind conditions, and by the consequent adjustments of the turbine yaw angle needed to maximize power production. Consequently, taking into account possible variations of the relative position between the LiDAR measurement volume and wake location, different measuring techniques were carried out in order to perform 2-D and 3-D characterizations of the mean wake velocity field. However, larger measurement volumes and higher spatial resolution require longer sampling periods; thus, to investigate wake turbulence tests were also performed by staring the LiDAR laser beam over fixed directions and with the maximum sampling frequency. The characterization of the wake recovery along the downwind direction is performed. Moreover, wake turbulence peaks are detected at turbine top-tip height, which can represent increased fatigue loads for downstream wind turbines within a wind farm.

scholarly journals Probabilistic and Risk-Informed Life Extension Assessment of Wind Turbine Structural Components

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 821
Author(s):  
Jannie Sønderkær Nielsen ◽  
Lindsay Miller-Branovacki ◽  
Rupp Carriveau
Keyword(s):  
Fatigue Life ◽  
Wind Turbine ◽  
Wind Farm ◽  
Assessment Method ◽  
Economic Feasibility ◽  
Life Extension ◽  
Structural Components ◽  
Safety Factors ◽  
Original Design ◽  
Partial Safety Factors

Reassessment of the fatigue life for wind turbine structural components is typically performed using deterministic methods with the same partial safety factors as used for the original design. However, in relation to life extension, the conditions are generally different from the assumptions used for calibration of partial safety factors; and using a deterministic assessment method with these partial safety factors might not lead to optimal decisions. In this paper, the deterministic assessment method is compared to probabilistic and risk-based approaches, and the economic feasibility is assessed for a case wind farm. Using the models also used for calibration of partial safety factors in IEC61400-1 ed. 4, it is found that the probabilistic assessment generally leads to longer additional fatigue life than the deterministic assessment method. The longer duration of the extended life can make life extension feasible in more situations. The risk-based model is applied to include the risk of failure directly in the economic feasibility assessment and it is found that the reliability can be much lower than the target for new turbines, without compromising the economic feasibility.

scholarly journals Wind Energy Assessment Using a Wind Turbine With Dynamic Yaw Control

2013 ◽  
Author(s):  
Md Nahid Pervez ◽  
Mehmet Sözen
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Dynamic Model ◽  
Wind Direction ◽  
Wind Farm ◽  
Lake Michigan ◽  
Electrical Energy ◽  
Energy Assessment ◽  
Yaw Angle ◽  
Electrical Energy Generation

In conducting the siting analysis of a possible on-shore or off-shore wind farm, computational tools are required to analyze the extensive wind data collected over long periods of time in order to estimate the energy that can be harnessed at that particular location. The major parameters that play a crucial role in this are the wind speed, wind direction, and presence of turbulence in the upcoming wind. However, estimation of the potential for electrical energy generation from wind at a particular site is quite complex and prone to error due to the uncertain nature of the wind. The yaw error, which is the difference between the direction of wind and the normal to the face of the rotor, can reduce the power output of a wind turbine significantly. Zero inertia assumption for the turbine rotor used by multiple assessment tools result in overestimation of the power output. For an accurate estimation of the energy that can be harnessed, the effect of directional change of the wind should be incorporated along with the other obvious parameters such as the wind speed, the effect of landscape and altitude. Most modern utility-scale wind turbines are equipped with yaw motion controller and direction measuring sensors that help change the yaw angle of the wind turbine to adjust for the wind direction. A dynamic control model and the corresponding scheme have to be incorporated in the energy estimation process. A wind energy assessment analysis for a potential off-shore wind farm in Lake Michigan is currently under way. An unmanned marine buoy, equipped with LIDAR-based data acquisition system, is deployed in Lake Michigan and data are measured at six different altitudes starting from 55 m and up to 175 m. As project participants, the authors have been working on developing a versatile, flexible and precise model and software tool to evaluate the potential for electrical energy generation. A MATLAB based program has been developed for this purpose, equipped with the capability of working with different data formats and different time averaged data sets. A dynamic model capable of considering the change in wind direction and adjusting the yaw angle has been developed as a part of the MATLAB program. The dynamic model evaluates the yaw error and implements a scheme for the adjustment of the orientation of the wind turbine in order to provide an accurate estimate of the amount of wind energy that can be converted into electrical energy. The algorithm for this dynamic model and the results obtained are discussed in this paper.

scholarly journals Computing the Real Impact of Wind Turbine Power Curve Upgrades: A SCADA-Based Multivariate Linear Method and a Vortex Generator Test Case

2018 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Mario Luca Fravolini ◽  
Silvia Cascianelli ◽  
Ludovico Terzi
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Linear Method ◽  
Vortex Generator ◽  
Test Case ◽  
Labor Costs ◽  
Linear Modeling ◽  
Data Set ◽  
The Real

Wind turbine upgrades have been spreading in the recent years in the wind energy industry, with the aim of optimizing the efficiency of wind kinetic energy conversion. This kind of interventions has material and labor costs and it is therefore fundamental to estimate realistically the production improvement. Further, the retrofitting of wind turbines sited in harsh environments might exacerbate the stressing conditions to which wind turbines are subjected and consequently might affect the residue lifetime. This work deals with a case of retrofitting: the testing ground is a multi-megawatt wind turbine from a wind farm sited in a very complex terrain. The blades have been optimized by installing vortex generators and passive flow control devices. The complexity of this test case, dictated by the environment and by the features of the data set at disposal, inspires the formulation of a general method for estimating production upgrades, based on multivariate linear modeling of the power output of the upgraded wind turbine. The method is a distinctive part of the outcome of this work because it is generalizable to the study of whatever wind turbine upgrade and it is adaptable to the features of the data sets at disposal. In particular, applying this model to the test case of interest, it arises that the upgrade increases the annual energy production of the wind turbine of an amount of the order of the 2%. This quantity is of the same order of magnitude, albeit non-negligibly lower, than the estimate based on the assumption of ideal wind conditions. Therefore, it arises that complex wind conditions might affect the efficiency of wind turbine upgrades and it is therefore important to estimate their impact using data from wind turbines operating in the real environment of interest.

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