FarmConners Wind Farm ControlBenchmark

Dec 16, 2021

Contents

1 Content 31.1 Provided Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Test Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3 Quantities of Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.4 General Taxonomy and Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.5 FAQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.6 Contact Us . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

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FarmConners Wind Farm Control Benchmark

The benchmark is closed now - Thanks to everyone who participated! We are currently working on the dissem-ination of the results - stay tuned! :)

Careful validation of the modelling and control actions is of vital importance to build confidence in the value ofcoordinated wind farm control (WFC). The efficiency of flow models applied to WFC should be evaluated to providereliable assessment of the performance of WFC. In order to achieve that, the FarmConners project launches a commonbenchmark for code comparison to demonstrate the potential benefits of WFC, such as increased power productionand mitigation of loads.

The online recording of the benchmark launch can be watched here.

The benchmark repository is located here.

The comparison of the WFC model results for the defined test cases will first be presented in WESC 2021 and thensent for publication in co-authorship with all the benchmark participants. All the registered participants receives a linkto a personalised cloud folder to update their results.

The benchmark builds on available data sets from previous and ongoing campaigns: synthetic data from high-fidelitysimulations, measurements from wind tunnel experiments, and field data from a real wind farm, see Provided DataSets.

The participating WFC models are first to be calibrated or trained using normal operation periods. For the blind test,both the axial induction and wake steering control approaches are included in the dataset and to be evaluated throughthe designed test cases. Three main test cases are specified, addressing the impact of WFC on single full wake, singlepartial wake, and multiple full wake, see Test Cases.

The WFC model outcomes will be compared during the blind test phase, through power gain and wake loss reductionas well as alleviation of wake-added turbulence intensity and structural loads. The probabilistic validation will bebased on the median and quartiles of the observations and WFC model predictions of the Quantities of Interest.

Every benchmark participant will be involved in the final publication, where the comparison of different tools willbe performed using the defined test cases. Instructions on how to participate are provided on how_to_participate.

Contents 1

FarmConners Wind Farm Control Benchmark

The FarmConners project has received funding from the European Union’s Horizon 2020 re-search and innovation programme under grant agreement No. 857844.

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CHAPTER 1

Content

1.1 Provided Data Sets

The FarmConners consortium agreed to build a test benchmark using available data sets from previous and ongoingWFC campaigns. These data sets are collected and released as common test benchmark case for code comparison. Theexamples of data processing and extraction for every Test Cases are provided in the GitLab repository. The benchmarkparticipants, see how_to_participate, will receive data access links per test case. However, if you are interested in thefull dataset, feel free to contact us.

The benchmark contains data from high-fidelity simulations, wind tunnel measurements, and a full field experimentconsisting down-regulation and wake steering operation periods. This variety allows comparison across site-specificdynamics.

The data set is divided into two periods to resemble field application of WFC models. The first period (calibration)is to be used to calibrate the control-oriented models for the normal operation data set, where both input and outputquantities of interest are provided. In the second period (blind test), the calibrated models are to be run blindly andonly the input signals are available. In that regard, the blind comparison is expected to show the overall performanceof the state-of-the-art WFC models, as well as the common trends and differences among them.

1.1.1 Synthetic data set - Large Eddy Simulations

Validation of control-oriented engineering models requires accurate reference data. In addition to the available fieldmeasurement data, which could be limited, high-fidelity large-eddy simulation (LES) tools provide a virtual wind-farmenvironment from which synthetic measurements can be taken under controlled conditions. They are often used tocalibrate (or train) and validate the lower fidelity (and computationally lower cost) models.

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FarmConners Wind Farm Control Benchmark

TotalControl LES:

Fig. 1: Layout of the Total Con-trol Reference Wind Power Plant(TC RWP). Axes are distances nor-malised by the rotor diameter, i.e.s/D, where D=178m.

One of the core activities within the TotalControl project is the development andvalidation of appropriate end-to-end wind-farm simulation models that cover thewhole chain from flow model over aero-elastic model to power-grid model. Inthis regard, a high-fidelity reference database is generated using two independentnumerical platforms: SP-Wind by KU Leuven and EllipSys3D by DTU.

The database is constructed for the TotalControl Reference Wind Power Plant(TC RWP) which consists of 32 turbines in a staggered pattern. The referenceturbines are the DTU 10 MW turbines, with a hub height of 119m and a rotordiameter of 178m. The database consists of simulations of different atmosphericconditions and different orientations of the TC RWP.

CL-Windcon LES:

The CL-Windcon project, as one of the major European research projects onwind farm control, has established different pillars for validation of farm modelsand control strategies. Among them, high-fidelity simulations have played arelevant role. Specifically, a set of SOWFA simulations has been performedand made accessible to the community through a public database. SOWFA,developed by NREL, is an open-source numerical simulation system employedfor the high-fidelity simulation of turbulent atmospheric flows together with theanalysis of wind plant and wind turbine fluid physics and structural response.The tool builds on top of the OpenFOAM Computational Fluid Dynamics (CFD)tool-kit and NREL’s aeroelastic wind turbine simulation tool OpenFAST.

Several combinations of wind speeds (3), turbulence intensities (3), and rough-ness lengths (3), together with yaw misalignment (up to ±30∘), and also de-ratings of wind turbines (in 5% power steps) have been computed at single windturbine (WT), 3WT and 9WT scenarios. The turbine model is the 10 MW windturbine developed in the INNWIND.EU project. Within this extensive dataset,the flow case with mean axial inflow 7.7 m/s, turbulence intensity 5.39% andshear height 0.001 m is selected, given the potential benefit reported in the liter-ature.

1.1.2 CL-Windcon WindTunnel Experiments

Data from experiments conductedin CL-Windcon project withinthe boundary layer test sectionof the Politecnico di Milanowind tunnel and with fully ac-tuated and sensorized scaledmodels is made available forthe benchmark exercise. Theexperimental setup is a scaledwind farm composed of up tothree scaled wind turbine models

was installed on the 13,m diameter turntable, which could be rotated to simulate different wind direction.

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The wind turbines used during the experiments are threeidentical G1 scaled models, whose rotor diameter, hubheight and rated rotor speed are 1.1m, 0.825m and850rpm, respectively. The models are equipped withactive pitch, torque and yaw control. They also fea-ture a comprehensive onboard sensorization, includingmeasures of shaft and tower bending moments, and theyhave been designed with the goal of achieving a realisticenergy conversion process and to support the develop-ment and testing of wind farm control strategies. Eachwind turbine model is also controlled by its own real-time modular Bachmann M1 system, where supervisorycontrol functions, pitch-torque-yaw control algorithms,and all necessary safety, calibration and data logging functions are implemented.

Within the wind tunnel, two different atmospheric boundary layers (ABLs) were simulated through the use of spiresplaced at the inlet of the test section. The two ABLs can be considered quite typical of both onshore and offshore sites(with neutral atmospheric stability) and are characterized by vertical wind profiles that are best-fitted by exponentiallaws with exponent 0.14 and 0.21, respectively for the offshore and onshore ABL. The turbulence intensities at hubheight are instead approx. 6% and 13% for the offshore and onshore ABL, respectively.

The available dataset consists of time series of several signals (rotor speed and azimuth, blades pitch, nacelle angle,shaft and tower loads) measured onboard each of the scaled wind turbine within the cluster, as well as time historiesof the flow speed measured, by means of three components hot-wire probes, at many locations within a single andmultiple wake shed by wind turbines operating at a wide range of conditions. Particularly, the single wake shed by ade-rated and yaw misaligned wind turbine has been traversed along horizontal and vertical lines at several distancesdownstream. Similarly, the wake shed after the second wind turbine of a 2-machines cluster has been traversed alonghorizontal lines.

1.1.3 Wind Farm Field Data

images/SMV_layout.png

Data from a commercial wind farm is made available for the bench-mark exercise. The wind farm is Sole du Moulin Vieux (SMV),which has been used for the wind farm control field tests of theSMARTEOLE research project. It is made of 7 wind turbines Sen-vion MM82 2.05MW on a non-complex terrain, though some localeffects can be observed, in particular due to the presence of a forestsouth of the farm.

The available dataset consists of twenty-nine months of 10-minstatistics (average, standard deviation, minimum and maximum val-ues) SCADA data covering the 20 most critical SCADA signals, for each wind turbine in the farm. Twelvemonths of data with wind farm under normal operation will be used for the calibration of the models, whilethe remaining dataset (including another twelve months of normal operation and five months during which bothwake steering and axial induction tests were led on turbine SMV6) will be used for the benchmark. 10-minstatistics data from a ground-based lidar Windcube V2 located closed to turbine SMV6 will also be provided sothat the misalignment of the SMV6 wind turbine can be precisely calculated for each 10-min during the wakesteering field test and be fed into any wake deflection model. Details on data availability is illustrated below.

1.1. Provided Data Sets 5

FarmConners Wind Farm Control Benchmark

1.1.4 Summary of Provided Datasets:

Data set Calibration Period Blind test Period Sampling rate Available signalsTotalControl LES(SP-Wind, EllipSys-3D)

40min 20min Normal Op.Additional WFCOp.

>1Hz Full Simulation:Flow & turbinesignals

CL-Windcon LES(SOWFA)

600 - 800s cases 600 - 800s cases Flow (10s), tur-bine(0.03s)

Flow & turbine sig-nals

CL-Windcon WindTunnel experiments

Several hours Several hours 250, 2500Hz Flow & turbine sig-nals

Wind Farm fielddata

12months 12months NormalOp. 5months WFCOp.

10min SCADA Windcubelidar

1.2 Test Cases

The test cases follow an increasing complex-ity level, i.e. starting from easier configura-tions (e.g. 1-2 turbines with quasi-stationaryflow and higher availability of information/dataregarding the input and output features ofthe models). The test cases then graduallyconverge to several turbines and real atmo-spheric conditions with relatively limited mea-surements with higher uncertainty. The col-lected results from all the test cases will befurther classified with respect to inflow con-ditions including wind speed and turbulenceintensity to assess the sensitivity to incomingflow conditions. Due to limited availability inconvection measurements/observations in theprovided dataset, the sensitivity analysis withrespect to atmospheric stability (and shear) isomitted from the benchmark.

1.2.1 Single Full Wake underWFC

The test case aims to investigate the quantities of interest in the singlewake behind a controlled turbine, for a fully aligned 2-turbine con-figuration. It should be noted that for some of the available dataset,the configuration is a subset of the wind farm, i.e. there are addi-tional blockage effects. The spacing between the turbines are alsovaried per data set. Both the axial induction and wake redirectionWFC approaches will be evaluated under this test case.

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Available data sets for that benchmark:

• Synthetic data set - Large Eddy Simulations:

– TotalControl LES: Ellipsys3D Actuator line (northerlywind rot90). 5-diameters distance between the tur-bines in a wind farm environment where turbines 29 and32 are yawed.

– CL-Windcon LES: subset of 3WT and 9WT scenarios.

• CL-Windcon Wind Tunnel Experiments: 5-diameters spacingbetween the wind turbines. Axial induction is implemented bymodifying the values of the reference rotor speed and torque.Wake steering WFC approach is conducted with the first ma-chine misaligned of −40∘ to 40∘ with steps of 10∘.

• Wind Farm Field Data: 3.7-diameters spacing between tur-bine pairs SMV6-SMV5. Axial induction is implemented viadown-regulated power curve. Wake steering WFC approach isconducted with 13∘ to 15∘ yaw misalignment of the first tur-bine.

Expected model results for that benchmark:

Here is the list of variables to upload the model results. They are to be uploaded to a personally assigned cloud folderto the registered benchmark participants, see how_to_participate. The participants are also required to provide a briefdescription of the model they have used to obtain the results, in any human-readable text format, including referencesto the relevant publications wherever applicable.

The variables are to be provided as time series, wherever possible. If the model used is limited to statistical propertiesonly, at least the median, mean, min, max and std of the listed variables should be uploaded per (binned) time period.

• Synthetic data set - Large Eddy Simulations:

– TotalControl LES: Both for Normal operation and under WFC

* Power produced by the turbines: P_29-P_25 and P_32-P_28

* Hub-height and/or rotor effective wind speed at the turbines: U_29-U_25 and U_32-U_28 and/orREWS_29-REWS_25 and REWS_32-REWS_28

* Flapwise root bending moment at the turbines: FlapM_29-FlapM_25 and FlapM_32-FlapM_28

* Total (vector sum) shaft bending moment at the turbines: ShaftM_29-ShaftM_25 andShaftM_32-ShaftM_28

* Total (vector sum) tower bottom bending moment at the turbines: TBBM_29-TBBM_25 andTBBM_32-TBBM_28

– CL-Windcon LES: Both for Normal operation and under WFC

* Power produced by the turbines: P_T0, P_T1

* Hub-height and/or rotor effective wind speed at the turbines: U_T0, U_T1 and/or REWS_T1,REWS_T2

* Flapwise root bending moment at the turbines: FlapM_T0, FlapM_T1

* Total (vector sum) shaft bending moment at turbines: ShaftM_T0, ShaftM_T1

* Total (vector sum) tower bottom bending moment at the turbines: TBBM_T0, TBBM_T1

1.2. Test Cases 7

FarmConners Wind Farm Control Benchmark

• CL-Windcon Wind Tunnel Experiments: Both for Normal operation and under WFC

– Power produced by the turbines: P_WT1, P_WT2

– Longitudinal wind speed at the wake measurement locations: U_wake1, U_wake2, ...

– Total (vector sum) shaft bending moment at the turbines: ShaftM_WT1, ShaftM_WT2

– Total (vector sum) tower bottom bending moment at the turbines: TBBM_WT1, TBBM_WT2

• Wind Farm Field Data: Both for Normal operation and under WFC

– Power produced by the turbines: P_SMV5, P_SMV6

– Hub-height and/or rotor effective wind speed at the turbines: U_SMV5, U_SMV6 and/or REWS_SMV5,REWS_SMV6

1.2.2 Single Partial Wake under WFC

In this test case, the objective is to assess the added value of imple-menting wake steering WFC approach in a two-turbine configurationwith lateral distance. Similar to the other scenarios, only the firstturbine in the 2-turbine wind farm configuration is controlled. Thespacings between the turbines are varied per data set.

Available data sets for that benchmark:

• Synthetic data set - Large Eddy Simulations:

– TotalControl LES: Ellipsys3D Actuator line (westerlywind rot00), 5-diameters axial and 2.5-diameters lat-eral distance between the nacelle positions where tur-bines 5 and 25 are yawed.

• CL-Windcon Wind Tunnel Experiments: 5-diameters longitu-dinal spacing between the wind turbines, 0.5-diameter lateralspacing. Wake steering WFC approach is conducted with thefirst machine misaligned of 30∘.

Expected model results for that benchmark:

Here is the list of variables to upload the model results. They are to be uploaded to a personally assigned cloud folderto the registered benchmark participants, see how_to_participate. The participants are also required to provide a briefdescription of the model they have used to obtain the results, in any human-readable text format, including referencesto the relevant publications wherever applicable.

The variables are to be provided as time series, wherever possible. If the model used is limited to statistical propertiesonly, at least the median, mean, min, max and std of the listed variables should be uploaded per (binned) time period.

• Synthetic data set - Large Eddy Simulations:

– TotalControl LES: Both for Normal operation and under WFC

* Power produced by the turbines: P_5-P_10-P_6 and P_25-P_30-P_26

* Hub-height and/or rotor effective wind speed at the turbines: U_10-U_6 and U_30-U_26 and/orREWS_10-REWS_6 and REWS_30-REWS_26

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* Flapwise root bending moment at the turbines: FlapM_5-FlapM_10-FlapM_6 and FlapM_25-FlapM_30-FlapM_26

* Total shaft bending moment at the turbines: ShaftM_5-ShaftM_10-ShaftM_6 andShaftM_25-ShaftM_30-ShaftM_26

* Total tower bottom bending moment at the turbines: TBBM_5-TBBM_10-TBBM_6 and TBBM_25-TBBM_30-TBBM_26

• CL-Windcon Wind Tunnel Experiments: Both for Normal operation and under WFC

– Power produced by the turbines: P_WT1, P_WT2

– Longitudinal wind speed at the wake measurement locations: U_wake1, U_wake2, ...

– Total (vector sum) shaft bending moment at the turbines: ShaftM_WT1, ShaftM_WT2

– Total (vector sum) tower bottom bending moment at the turbines: TBBM_WT1, TBBM_WT2

1.2.3 Multiple Wake under WFC

The test case aims to investigate the quantities of interest in a rowof turbines. Depending on the dataset, either only the first turbineor a number of upstream turbines in the wind farm are controlled.The spacings between the turbines are not necessarily uniform, andvaried per data set. Both the axial induction and wake redirectionWFC approaches will be evaluated under this test case.

Available data sets for that benchmark:

• Synthetic data set - Large Eddy Simulations:

– TotalControl LES: Ellipsys3D Actuator line (northerlywind rot90). 5-diameters distance between the tur-bines in a wind farm environment where turbines 29 and 32 are yawed.

– CL-Windcon LES: 3WT and 9WT scenarios

• CL-Windcon Wind Tunnel Experiments: 3-turbine configura-tion with uniform 5-diameters spacing. Axial induction isimplemented by modifying the values of the reference rotorspeed and torque. Wake steering WFC approach is conductedwith the first machine misaligned of 30∘.

• Wind Farm Field Data: 6-turbine configuration with 3.7 and4.3-diameters spacing, i.e. SMV6 – SMV1.

Expected model results for that benchmark:

Here is the list of variables to upload the model results. They are to be uploaded to a personally assigned cloud folderto the registered benchmark participants, see how_to_participate. The participants are also required to provide a briefdescription of the model they have used to obtain the results, in any human-readable text format, including referencesto the relevant publications wherever applicable.

The variables are to be provided as time series, wherever possible. If the model used is limited to statistical propertiesonly, at least the median, mean, min, max and std of the listed variables should be uploaded per (binned) time period.

• Synthetic data set - Large Eddy Simulations:

1.2. Test Cases 9

FarmConners Wind Farm Control Benchmark

– TotalControl LES: Both for Normal operation and under WFC, 8-turbine rows (behind turbines 29 and32) i.e. i=29,25,21,17,13,9,5,1 and i=32,28,24,20,16,12,8,4

* Power produced by the turbines: P_i

* Hub-height and/or rotor effective wind speed at the turbines: U_i and/or REWS_i

* Flapwise root bending moment at the turbines: FlapM_i

* Total shaft bending moment at the turbines: ShaftM_i

* Total tower bottom bending moment at the turbines: TBBM_i

– CL-Windcon LES: Both for Normal operation and under WFC, 3WT and 9WT layouts n=3 or n=9

* Power produced by the turbines: P_Ti, where i=0,n-1

* Hub-height and/or rotor effective wind speed at the turbines: U_Ti and/or REWS_Ti, where i=0,n-1

* Flapwise root bending moment at the turbines: FlapM_Ti, where i=0,n-1

* Total shaft bending moment at the turbines: ShaftM_Ti, where i=0,n-1

* Total tower bottom bending moment at the turbines: TBBM_Ti, where i=0,n-1

• CL-Windcon Wind Tunnel Experiments: Both for Normal operation and under WFC, 3WT layout n=3

– Power produced by the turbines: P_WTi

– Longitudinal wind speed at the wake measurement locations: U_wake1, U_wake2, ...

– Total (vector sum) shaft bending moment at the turbines: ShaftM_WTi

– Total (vector sum) tower bottom bending moment at the turbines: TBBM_WTi

• Wind Farm Field Data: Both for Normal operation and under WFC, SMV wind farm 7WT layout n=7

– Power produced by the turbines: P_SMVi, where i=1,n

– Hub-height and/or rotor effective wind speed at the turbines: U_SMVi and/or REWS_SMVi, where i=1,n

1.3 Quantities of Interest

The performance of the WFC model participating in the benchmark is to be evaluated based on wind speed, powerand turbine response criteria. It should be noted that the models should be run according to their capabilities. Due tothe control-oriented character of the benchmark, mainly steady-state and quasi-dynamic low-cost models are expectedto provide solutions. However, depending on the model fidelity, the statistical quantities, i.e. mean, median, standarddeviation, etc. from time series of 3-dimensional flow fields can also be extracted.

The resulting quantities of interest for the benchmark are listed below. In order to provide a generic overview, themetrics to be used for validation and comparison are non-dimensionalised. The statistical metrics to be used for thesequantities are the median and quartiles of both the observation and simulation ensemble. The uploaded results will beprocessed by the benchmark organisers to diagnose quantities of interest with the publicly released notebooks on theGitLab repository.

For the final analysis of the quantities of interest, the flow and turbine response behaviour under WFC are to becompared with the normal operation conditions. Therefore, for a fair comparison, it is of utmost importance that theinflow for both WFC and normal operational cases are statistically similar.

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1.3.1 Power gain

Commonly referred in literature, the potential power gain under WFC should be assessed with respect to the normaloperation for the whole wind farm in question.

∆𝑃 =(∑︀𝑛

𝑖=1 𝑃𝑖)𝑂𝑝.=𝑊𝐹𝐶

(∑︀𝑛

𝑖=1 𝑃𝑖)𝑂𝑝.=𝑁𝑜𝑟𝑚𝑎𝑙

− 1

𝑛 is the total number of turbines considered in the test case, 𝑃 is the power produced by turbine 𝑖, and 𝑂𝑝. = 𝑊𝐹𝐶and 𝑂𝑝. = 𝑁𝑜𝑟𝑚𝑎𝑙 correspond to operation under WFC and normal operation, respectively.

1.3.2 Wake loss reduction

Although highly correlated to power gain, the potential reduction in the wake losses under WFC is another quantityof interest for the WFC technology stakeholders. This is mainly due to the more scalable character of the wake lossreduction from simpler 2-turbine configurations to more complex wind farm layouts. It also has the benefit to be ableto partially mitigate wind-to-power conversion uncertainties for certain WFC models.

∆𝑢 =

∑︀𝑛−1𝑗=1 (𝑈𝑢𝑝 − 𝑈1+𝑗)𝑂𝑝.=𝑁𝑜𝑟𝑚𝑎𝑙 −

∑︀𝑛−1𝑗=1 (𝑈𝑢𝑝 − 𝑈1+𝑗)𝑂𝑝.=𝑊𝐹𝐶

𝑈𝑢𝑝

∆𝑢 is the non-dimensionalised wake loss at the wind farm level, 𝑈 is either the hub height or rotor effective windspeed, 𝑅𝐸𝑊𝑆, that represents the spatially averaged wind speed over the rotor; at the upstream, 𝑈𝑢𝑝, and downstreamturbine(s), 𝑈1+𝑗 .

1.3.3 Reduction in wake-added Turbulence Intensity

The added TI is an important feature for the wind farm flow, which increases both the variability in power productionand structural loads of the downstream turbine(s). Accordingly, a potential reduction in wake-added TI with WFCtechniques is a quantity of interest.

∆𝑇𝐼𝑖 =

(︂𝜎𝑈𝑖

𝑈𝑖

)︂𝑂𝑝.=𝑁𝑜𝑟𝑚𝑎𝑙

−(︂𝜎𝑈𝑖

𝑈𝑖

)︂𝑂𝑝.=𝑊𝐹𝐶

where 𝑖 = 2, 𝑛

1.3.4 Load Alleviation

One of the important use cases for WFC is the mitigation of structural loads on the turbines. In order to evaluate themodel performance on potential load reduction under WFC in the defined test cases, the validation and the comparisonof the models will be based on the normalised Damage Equivalent Loads (DEL) of the flapwise root bending moment𝐹𝑙𝑎𝑝𝑀 , total shaft bending moment 𝑆ℎ𝑎𝑓𝑡𝑀 and total tower bottom bending moment 𝑇𝐵𝐵𝑀 . Similar to powergain and wake loss reduction metrics, the reduction in loads is assessed in comparison to the normal operation. Notethat the dataset to assess this quantity of interest is limited to Synthetic data set - Large Eddy Simulations and CL-Windcon Wind Tunnel Experiments, as the available field measurements are deemed to be inconclusive for such ananalysis at this stage.

∆𝐷𝐸𝐿 𝐹𝑙𝑎𝑝𝑀𝑆ℎ𝑎𝑓𝑡𝑀𝑇𝐵𝐵𝑀

= 1 −

⎛⎜⎜⎜⎝𝐷𝐸𝐿 𝐹𝑙𝑎𝑝𝑀

𝑆ℎ𝑎𝑓𝑡𝑀𝑇𝐵𝐵𝑀 𝑂𝑝.=𝑊𝐹𝐶

𝐷𝐸𝐿 𝐹𝑙𝑎𝑝𝑀𝑆ℎ𝑎𝑓𝑡𝑀𝑇𝐵𝐵𝑀 𝑂𝑝.=𝑁𝑜𝑟𝑚𝑎𝑙

⎞⎟⎟⎟⎠𝑖

i = 1, n

1.3. Quantities of Interest 11

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1.4 General Taxonomy and Nomenclature

The descriptions for general abbreviations and symbols referred in the benchmark are listed here. The data specificabbreviations, e.g. description of the data channels, are attached to the dataset sent to the registered benchmarkparticipants.

Abbreviation or Symbol Description, as referred withinthe benchmark

Unit

WFC Wind Farm (flow) Control •

TC RWP TotalControl Reference WindPower Plant

•

CL-Windcon CL-Windcon: EU H2020 project onClosed Loop Wind Farm Control

•

SMV Sole du Moulin Vieux wind farm •

WT Wind turbine •

𝑂𝑝. = 𝑊𝐹𝐶 Operation under WFC •

𝑂𝑝. = 𝑁𝑜𝑟𝑚𝑎𝑙 Normal (or greedy) operation •

P_i Power produced by turbine i withinthe wind farm

kW

U_i Hub-height wind speed at turbine iwithin the wind farm

m/s

REWS_i Rotor Effective Wind Speed at tur-bine i within the wind farm

m/s

∆𝑢 Non-dimensionalised wake loss •

𝑇𝐼 Turbulence Intensity •

𝐷𝐸𝐿 Damage Equivalent Loads kNmFlapM_i Flapwise root bending moment at

turbine ikNm

ShaftM_i Total shaft bending moment at tur-bine i

kNm

TBBM_i Total tower bottom bending momentat turbine i

kNm

min minimum of a given variable in agiven time period

•

max maximum of a given variable in agiven time period

•

std standard deviation of a given vari-able over a given time period

•

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1.5 FAQ

Here you can find answers to frequently asked questions. If you need further assistance, feel free to Contact Us.

Why should I participate? FarmConners WFC benchmark is designed as a consensus of all the stakeholders in thefield and it provides a unique opportunity in assessing the performance of the state-of-the-art WFC-orientedmodels. It provides a platform to compare your results with your colleagues in the WFC field. Additionally, asa participant it allows you to collaborate further with others in the community and be a part of advancement inthe WFC technology.

Is there a deadline for officially joining the benchmark? There is no deadline for registration itself but we do havea deadline for result submission on 15/02/2021 (extended). In order to get access to the data, as well as itsspecific documentation, and start getting familiar with it, we suggest as early registration as possible.

Is there any rule or limitation as to how many test cases or datasets we can choose? Do we need to select a dataset for each test case, or at least one full test case, etc..There is no upper or lower limit in the test cases or datasets per test cases you would like to investigate. Youcan choose as many test cases and as many datasets per test case as you’d like.

Should we end up including the LES or wind tunnel datasets, would we then be required to provide results also for the load alleviation aspect, or could we opt out from this one?We expect participants to run their models according to their capabilities. Meaning that if you don’t get theload output from the setup you use for the benchmark, you can opt out for those. In short, we expect at least thewind speed and power output from the models, load components are optional.

Are the submissions and potential publications of the results anonymised? Absolutely. We are assigning a par-ticipant ID per person and while compiling the results for publication(s), we will likely collect the models undercertain tags as well (e.g. there could be several participants using FLORIS). The labels will then look like patic-ipantXX_modelYY in the results. As a participant, you will know your own participant ID hence you can stillsee how your model(s) compare to others. We will then get back to all the participants to check if they wouldprefer not to appear as a co-author in the publication(s), when the results are ready.

For any given dataset, would we be allowed to submit multiple sets of results based on different models, or would we need to choose just one?Participants are in fact encouraged to apply more than one model to the BLIND tests. The compiled resultswould then look like, for example, participantX1_modelY1, participantX1_modelY2, etc.

Can we run our model based on different control strategies? The FarmConners WFC benchmark aims to test themodel performance under given WFC strategy, rather than to optimise performance under WFC. Correspondingcontrol inputs are given in the provided datasets. However, there is an AEP optimisation exercise in Wind FarmField Data where you can implement any control strategy of your choice. Further information on the exerciseis included in the documentation forwarded to the participants of Wind Farm Field Data test cases.

1.6 Contact Us

For general questions regarding the benchmark: Tuhfe Göçmen

For TotalControl LES dataset - SP-Wind: Ishaan Sood , EllipSys3D: Søren Juhl Andersen

For CL-Windcon LES dataset: Irene Eguinoa Erdozain & Oscar Eduardo Pires Jimenez

For CL-Windcon Wind Tunnel Experiments: Filippo Campagnolo

For Wind Farm Field Data: Thomas Duc

1.5. FAQ 13