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Renewable Energy 62 (2014) 379e387

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Renewable Energy

journal homepage: www.elsevier .com/locate/renene

Development of a 20 kW wind turbine simulator with similarities to a3 MW wind turbine

Ki-Yong Oh a,b, Jae-Kyung Lee b, Hyung-Joon Bang c, Joon-Young Park b, Jun-Shin Lee b,B.I. Epureanu a,*

aDepartment of Mechanical Engineering, University of Michigan, 2350 Hayward Street, Ann Arbor, MI 48109-2125, USAb Technology Commercialization Office, KEPCO Research Institute, Daejeon 305-760, South KoreacWind Energy Research Center, Korea Institute of Energy Research, Daejeon 305-343, South Korea

a r t i c l e i n f o

Article history:Received 1 August 2012Accepted 23 June 2013Available online

Keywords:Condition monitoring systemFault diagnosisPrognosisSimulatorWind turbine

* Corresponding author. Tel.: þ1 734 647 6391; faxE-mail address: epureanu@umich.edu (B.I. Epurea

0960-1481/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.renene.2013.06.026

a b s t r a c t

As the use of wind power has steadily increased, the importance of a condition monitoring and faultdiagnosis system is being emphasized to maximize the availability and reliability of wind turbines. Todevelop novel algorithms for fault detection and lifespan estimation, a wind turbine simulator isindispensible for verification of the proposed algorithms before introducing them into a health moni-toring and integrity diagnosis system. In this paper, a new type of simulator is proposed to develop andverify advanced diagnosis algorithms. The simulator adopts a torque control method for a motor andinverter to realize variable speed-variable pitch control strategies. Unlike conventional motoregeneratorconfigurations, the simulator includes several kinds of components and a variety of sensors. Specifically,it has similarity to a 3 MW wind turbine, thereby being able to acquire a state of operation that closelyresembles that of the actual 3 MWwind turbine operated at various wind conditions. This paper presentsthe design method for the simulator and its control logic. The experimental comparison between thebehavior of the simulator and that of a wind turbine shows that the proposed control logic performssuccessfully and the dynamic behaviors of the simulator have similar trends as those of the wind turbine.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Awind turbine is a system that converts kinetic energy from thewind into electric energy through its generator. Wind turbines arebecoming the fastest growing source of energy and are receivingworldwide attention due to the technological advancements inharnessing wind power. However, since the nacelle of multi-MWwind turbines is located at a height of dozens of meters, unex-pected failures of gearboxes, generators or blades require longwaiting periods and large cranes for repair or replacement. Theseusually cause higher maintenance costs compared to other powersystems and economic loss due to cessation of power productionduring turbine downtime.

Considering the time and cost involved in awind turbine failure,countermeasures are necessary for ensuring reliability and eco-nomic feasibility of wind energy conversion systems. For this pur-pose, condition monitoring systems (CMS) can be installed in

: þ1 734 615 6647.nu).

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multi-MW wind turbines. In particular, CMS which include ablade monitoring system e blade health monitoring and integrityevaluation system (BHMIES) e are considered especially indis-pensible for offshore wind turbines due to the difficulties ofreaching offshore sites and due to the maintenance-related con-straints caused by weather conditions [1]. Diagnosis and prognosisalgorithms based on diverse sensors and signal processing tech-niques have been developed to increase the reliability and accuracyof CMS [2]. In developing and validating condition monitoring andfault diagnosis algorithms, a wind turbine simulator is an excellenttool for verification due to practical difficulties of applying the al-gorithms directly to operating wind farms.

Several in-depth studies of wind turbine simulators have beenconducted [3e5]. Kojabadi et al. developed a wind turbine simu-lator to create a controlled test environment for drive trains of windturbines [6]. Choy et al. developed a real-time hardware simulatorto analyze a grid-tied wind power system with a permanentmagnet synchronous generator (PMSG) [7]. These simulators have asimple motoregenerator configuration, or are composed of com-ponents only necessary to achieve their development objectives.Such simulators are used for performance evaluation of only a

Table 1Design parameters for the wind turbine simulator.

WinDS3000 Simulator

Scale factor 150Gear ratio 92.9 90.0Equivalent moment

of inertia [kg m2]10,480,040.0 43.1

Inertia timeconstant [s]

4.7

Part Rotorside

Generatorside

Rotorside

Generatorside

Rated [rpm] 15.7 1458.8 20.0 1800.0Rated torque T [kN m] 1824.706 19.638 9.549 0.106Rated power [kW] 3000.0 20.0

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387380

single component from the gearbox, generator or inverter accord-ing to their individual design, and for evaluation of failure effects onthe power system. For ensuring stable experiments, speed controlof the entire system was usually performed.

However, all the simulators discussed above are inappropriatefor developing conditionmonitoring and fault diagnosis algorithmsdue to the following reasons: first, these systems do not have thegeneral configuration of a wind turbine system that consists ofturbine blades, a step-up gearbox and a generator, although mostaccidents have been caused by blades, gearboxes and generators inwind turbines [8]; and second, the acquisition of non-stationarysignals could not be achieved under a wide range of operatingconditions. To this end, torque control should be used on the entiresystem by adopting a variable speed-variable pitch control strategy.

To solve the limitations of existing simulators, Korea ElectricPower Corporation (KEPCO) Research Institute developed a novelwind turbine simulator suitable for the development and verifica-tion of condition-monitoring and fault diagnosis algorithms. Thissimulator was equipped with wind turbine main components anddesigned to operate with the same control logic as an actual windturbine so that the non-stationary operating states can be simu-lated. In addition, appropriate sensors were installed for acquisitionof data from each component. The dynamic characteristics of tur-bines and wind turbulence were simulated by applying a modelhaving similarities with a 3 MW wind turbine. Based on thesesimilarities, the torque control curves for the motor and thegenerator were calculated. For a comparative analysis between thesimulator and the turbine, actual turbine data were measured byCMS [9] and BHMIES [10,11], which were installed in a 3 MW windturbine WinDS3000 (Doosani Heavy Industries and Construction)at the YeongHeung wind farm. The comparison between thesimulator and the wind turbine through experiments shows thatthe proposed control logic performs successfully and the simulatorshows similar tendencies to the actual wind turbine under variouswind conditions.

2. Design criteria

The proposedwind turbine simulator shouldmeet the followingrequirements in order to simulate the diverse operating conditionsof an actual wind turbine.

First, the simulator should be equipped with blades, a gearboxand a generator. That is because these components account formore than 90% of the maintenance cost and downtime of a windturbine [8]. These components are required in order to obtain sig-nals for monitoring the health condition of the turbine.

Second, the simulator should operate based on the same controllogic as an actual wind turbine in order to acquire non-stationarysignals by simulating diverse operating conditions. In wind tur-bines, the thrust force and the torque are created in the rotor by thewind. The torque is transferred to the generator to extract as muchenergy from the wind as possible as shown in Eq. (1) below:

Tr � Tg ¼ I _u; (1)

where Tr is the torque created in the rotor by the wind (energyinput), Tg is the torque in the generator (energy extracted), I ismoment of inertia with respect to the axis z of rotation of the tur-bine, u is the angular velocity of the rotor. In addition, blade pitchcontrol should be conducted so that power is maintained approx-imately constant and excessive mechanical loads at higher thanrated wind speeds are prevented. The simulator should apply avariable speed-variable pitch control strategy to this end.

Finally, there should be similarity between multi-MW windturbines and the simulator because the performance and reliability

of the developed algorithm can only be verified when the simulatorshows dynamic characteristics similar to those of real wind tur-bines under diverse wind and operating conditions. In this study,the wind turbine WinDS3000 (Doosan Heavy Industries and Con-struction) was selected as a target. The design parameters areestimated from Eqs. (2)e(4) below [12,13]:

Ho ¼ Iou2o

2Po¼ Hs ¼ Isu2

s2Ps

; (2)

lo ¼ uoRovo

¼ ls ¼ usRsvs

; (3)

u3oR

5o ¼ su3

sR5s ; (4)

where H(�), P(�), l(�), R(�), v(�) denote the inertia time constant, po-wer, tip speed ratio (TSR), radius of the blade and rated wind speed,respectively. The subscripts o and s indicate the original system andthe scaled-down simulator, respectively. Table 1 shows the resultsof the calculated design parameters for the simulator by using theones for the WinDS3000 turbine and Eqs. (2)e(4). A scale factor of150 was chosen by considering installation conditions and costs.Also, a gear ratio of 90 was chosen.

3. Design of simulator

3.1. Overall structure

Fig. 1 shows the configuration of the entire system. To simulatethe desired wind conditions, the aerodynamic torque is applied byusing a DC motor. The rotor of the wind turbine rotates at a lowspeed of approximately 20 rpm even though it can experience alarge torque resulting from the rotor blade inertia. Manufacturingcosts of a low-speed, high-torque motor are high. Hence, thesimulator was designed to apply a low-speed, and a high-torque tothe rotor by using a 1:90 reduction gearbox located in the rear sideof the motor.

For the development of condition monitoring and fault detec-tion algorithms, the core components to be monitored e namelythe rotor, gearbox and generator e were placed in the rear side ofthe reduction gearbox in consecutive order. The rotor was designedto receive torque by using a timing belt to prevent vibrationtransmission from the rotor to other components. The reason forthis rotor installation is to allow measurements of the strain vari-ation caused by time-varying moments in operation. The selectedgearbox has a three stage 2-planetary/1-helical concept. Also, aPMSG type generator was selected because it is used for large windturbine, it is efficient, and has a good power-to-size ratio andminimum maintenance. A full power, back-to-back inverter isimplemented in the simulator to connect the grid and control the

Fig. 1. Simulator configuration.

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387 381

generator. In addition, a reduction gearbox with a gear ratio of 1:13was installed in front of the generator so that the generator rpme

torque curve could be located under the performance curve of theselected generator.

3.2. Rotor

The simulator rotor consists of blades and a hubwhich equip thepitch control system and interrogator. An optic strain sensor e

namely a fiber Bragg grating (FBG) strain sensor e was embeddedin the longitudinal direction of the blade to observe the dynamicbehavior of the blade. The collective pitch control system is realizedfor a variable speed-variable pitch control strategy. The interrogatorcollects measured optic data and transmits it to a supervisorycontroller.

The blade of the simulator should have similarities to that of anactual wind turbine under diverse operating conditions. Thesimulator blade was designed with parameters shown in Table 2which have the same cut-in wind speed, rated wind speed, cut-out wind speed and rated angular velocity as the WinDS3000turbine.

Each blade was designed using an objective function to assign amaximum value to the annual energy production (AEP). Varioustypes of airfoil sections similar to those of a multi-MW wind

Table 2Design parameters for blades.

1 m Span blade

Rated wind speed 12 m/sCut-in wind speed 3 m/sCut-out wind speed 25 m/sNumber of blades 3Radius 1050 mmControl type Pitch controlRated rotating speed 15.7 rpm

turbine were applied. Considering that the moment of inertia of ablade is relatively small, a foam sandwich skin structure wasadopted for smooth signal acquisition under small loads. Fivesensors were placed at five points where significant displacementchanges take place under diverse operating conditions. These fivelocations were chosen after a careful dynamic load analysis [14].Fig. 2 shows the shape of a designed blade and FBG sensor layout.

Since modern wind turbines have active control systems toprevent excessive loads and control power efficiently in powerregulation region, a collective pitch control system was realized byusing a bevel gear and two photo interrupters. The two photo in-terrupters were placed at intervals of 90�circumferentially so thatthe pitch angle can operate from 0� to 90�. A blade connector wasdesigned using a hollow shaft to connect the FBG line to theinterrogator. As shown in Fig. 3, a pitch controller was installed infront of the hub. The pitch controller regulates the DCmotor using aPID technique where the input is a command for pitch angle fromthe supervisory controller.

Fig. 2. Design of the blade shape and the FBG sensor layout.

Fig. 3. Design of the rotor.

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387382

3.3. Gearbox

Like the WinDS3000 turbine, a 3 stage 2-planetary/1-helicalgearbox was adopted for the simulator. The gear ratio was set to 90considering that the usual gear ratio of multi-MW wind turbine is90e95. Details of the gearbox structure and sensor configurationsare shown in Fig. 4.

Accelerometers were installed to measure the condition of thegearbox and to evaluate similarities to the WinDS3000 turbine.Considering that gearbox damage occurs in the bearing and teeth[2], the accelerometer was installed at each stage as well as theinput bearing.

4. Design of control algorithm

Fig. 5 shows the configuration of the control logic of the windturbine simulator. The turbulent wind profile is created first. Next,themotor torque and the generator torquewhich correspond to thewind speed are applied. At that time, the generator torque iscalculated by using a virtual wind turbine to describe the dynamicbehavior of the wind turbine. The blade pitch angle is controlledwhen reaching the power regulation region.

4.1. Turbulent wind profile

To operate the simulator, the turbulent wind profile is generatedfirst. In this paper, the Kaimal model recommended by Interna-tional Electrotechnical Commission (IEC) [15] was used, as shownin Eq. (5) below:

Skðf Þ ¼ 4s2kLK=Vhub

ð1þ 6fLK=VhubÞ5=3; (5)

Fig. 4. Gearbox structure and accelerometer installation layout.

where Sk is the single-sided velocity component spectrum, f is thefrequency in Hertz, subscript k is an index referring to the directionof the velocity component, sk is the standard deviation of the ve-locity component, LK is the integral scale parameter of the velocitycomponent, and Vhub is thewind speed at the hub height. To changethe power spectrum density of the wind velocity into turbulentwind in the time domain, Eqs. (6) and (7) below are used:

vðtÞ ¼ vmean þXN

i¼1

Aisinð2pfit þ FiÞ; (6)

Ai ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiSðfiÞDf

q; (7)

where Ai and fi, denote the magnitude and frequency of the ithcomponent of the spectrum, and Fi is a random phage angle in therange [�p, p].

4.2. Virtual wind turbine model

When turbulent wind is created, a corresponding aerodynamictorque is applied to the rotor. Awind turbine does not conduct pitchcontrol below the ratedwind speed because that is the rangewherethe maximum Cp is tracked. Accordingly, l calculated by Eq. (3) isconstant regardless of the wind speed, and it is possible to estimatea reference torque curve for rotor from the generator torque curveusing Eqs. (8) and (9) regardless of aerodynamic information for theblades, as shown in Fig. 6. One obtains,

Tr ¼ ng:b:Tghg:b:

; (8)

ur ¼ ug

ng:b:; (9)

where ng.b. denotes gear ratio and hg.b. denotes efficiency of thegearbox.

In the calculated reference torque curve for rotor, the torqueconstant (kop) can be estimated by using regression analysis [16].The dependence of the rotor torque on the wind speed can becalculated using a power curve and Eq. (10) below:

Tr ¼ kopU2r : (10)

The calculated rotor torque for a given wind speed is a steady-state quantity. Nonetheless, transient responses can be realizedby capturing the dynamic characteristics of the rotor. In a previousstudy, the dynamic characteristics of the rotor were described byinstalling a balance plane [17]. Distinct from that study, the currentsimulator was simplified through the substantialization of a virtualwind turbine model. Since the objective of installing a balanceplane is to describe the inertia of the wind turbine, the wind tur-bine is modeled using Eq. (11) and the transfer function shown inEq. (12) [18] below:

IeqdUr

dtþ BeqUr ¼ Tr � Tg; (11)

GðsÞ ¼ KHsþ 1

; (12)

where Ieq is equivalent moment of inertia, Beq is equivalentdamping constant and K is the gain constant. The inertia timeconstant for Eq. (12) can be calculated by using the equivalentmoment of inertia and the equivalent damping constant from Eqs.

Fig. 5. Schematic diagram of simulator control logic.

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387 383

(11) or (2). Therefore, when the aerodynamic torque calculatedthrough regression analysis passes through the inertia filter of Eq.(12), the dynamic characteristics of the wind turbine are obtainedas shown in Fig. 7. Although wind has the characteristic of turbu-lence, namely it fluctuates in time, the torque from the rotor has atime delay due to the inertia of the rotor. Thus, the high-frequencycomponent disappears. It can be assumed that the aerodynamictorque and the angular velocity do not change rapidly due to suchslow dynamic characteristics.

4.3. Estimation of motor/generator torque curve

A wind turbine starts power generation at a cut-in wind speed,and is controlled to track the maximum Cp curve before reachingthe rated wind speed. It conducts inverter control so that it canmaintain constant power by using Eq. (13) below, and performsblade pitch control to prevent excessive aerodynamic torque andmechanical loads above the rated wind speed [19].

Tr ¼ Pratedur

: (13)

Fig. 6. Angular velocityetorque cur

Since the maximum Cp tracking region has constant l andconstant pitch angle, the rotor torque curve can be converted usingEqs. (2)e(4). At a wind speed over the rated power, the rotor torquecurve can be estimated by using Eq. (13). The calculated rotor tor-que curve can be converted into the generator torque curve bytaking into consideration the efficiency and the gear ratio of twogearboxes, as shown in Fig. 8. In Fig. 8, the dotted line shows thegenerator performance curve. The supervisory controller sends thereference torque curve to the inverter and the inverter controlsgenerator torque by using PI control with this signal in a similarway to multi-class wind turbine. The gains for the PI controller aredetermined by trial and error method. In general, the PI controlleris used in power plants such as wind turbines, gas turbines and soon due to excellent stable control performance.

The calculated generator torque curve is converted into motortorque curve for any motor angular velocity by taking intoconsideration the gear ratio and the efficiency of the three gear-boxes, as shown in Fig. 9(a). The motor torque curve is convertedinto motor input torque for any wind speed using the power curveand Eq. (10) at the maximum Cp in the tracking region, as shown inFig. 9(b). The supervisory controller sends the reference torque

ve of the WinDS3000 turbine.

Fig. 7. Estimated rotor torque reflecting rotor dynamic characteristics.

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387384

curve to the motor to its control torque. The motor controller usesPI control in the same way as the inverter for stabilization of theentire system. The gains of the PI controller for the motor are alsotuned with trial and error method.

4.4. Pitch control

The inverter controls the electrical power to maintain constantpower in the power regulation regions, as shown in Eq. (13). Fromthe mechanical control perspective, the load is adjusted throughblade pitch control as to maintain a constant angular velocity. Ingeneral, the actual wind turbine has a nonlinear behavior in thepower regulation region. Hence, the state equation is linearized ateach operation point and gain scheduling is conducted byextracting control constants for each operation point. Accordingly,aerodynamic data for the blades is necessary for accurate simula-tion. In this study, the blade angle for each wind speed was ob-tained by using a look-up table as shown in Fig. 10. The look-uptable was used because the aerodynamic information of theWinDS3000 blades was not available. The pitch controller operatesonly in the power regulation region. Hence, it was designed so thatit could control the pitch angle based on the generator angularvelocity when the generator power was over 20 kW. The pitchcontroller adopts PID control method for accurate adjustment ofblade pitches, and ZieglereNichols method was used for its gaintuning.

Fig. 8. Generator torqueespeed curve.

5. Experiments

In this section, the proposed control logic is tested first forreliability verification. Next, comparisons between the proposedsimulator and theWinDS3000 turbine installed in the YeongHeungwind farm are provided to verify the similarities with accelerationsmeasured at the gearbox and strains measured in the blades. Fig. 11shows a picture of the wind turbine simulator developed in KEPCOResearch Institute.

5.1. Control logic

The designed control logic was verified by testswhere themotortorque of the simulator was increased. Since the inertia filter wasexcluded in this test and since the actual inertia of the simulator issmall, the angular velocity may change rapidly and the systemmaynot be well controlled if there is an error in the estimated values forthe motor torque curve and the generator torque curve.

Fig. 12(a) shows experimental results. The left axis shows thegenerator torque and the right axis shows the pitch angle. It can beobserved that the simulator follows the reference curve in theentire control region. It can also be observed that the simulatordoes not perform pitch control below the rated power but it doesperform pitch control above the rated wind speed.

Fig. 12(b) shows the generator power and the generator angularvelocity with respect to time. The left axis shows the generatorpower, and the right axis shows the generator angular velocity. Itcan be observed that the angular velocity increases rapidly whenthe simulator starts operating (due to its initial/start-up angularvelocity) and slowly increases after that. In addition, it can beobserved that the angular velocity of the generator changes rapidlywithin the 10 s from time 100 s to 110 s. During this time, the motortorque is intentionally changed to check if the control logic is stableand maintains the constant power. When the motor torque variesin the power regulation region, the torque is varied in inverseproportion to the angular velocity. Also, the pitch control of theblades is performed, as shown in Fig. 12(a). In addition, thegenerator output increases stably and reaches the rated powerwhich remains constant although there are torque variations in therated output maintenance area, as shown in Fig. 12(b).

It can be concluded from the experimental results that thevalues of the motor torque and those of the generator torqueestimated at the maximum Cp in the tracking region are correct andthe inverter and pitch controller operate stably in the powerregulation region. Accordingly, the wind turbine simulator cansimulate torque and pitch control of an actual wind turbine for theentire control domain.

5.2. Similarity

Fig. 13 shows the acceleration measured for the WinDS3000turbine and that measured for the simulator gearbox output side inthe horizontal direction with respect to the wind speed. The valuesshown for the WinDS3000 turbine are the mean and standarddeviation of the RMS of the measured acceleration for 600 smeasured by the CMS installed in the YeongHeung wind farm 6from November 2011 to April 2012. The values shown for thesimulator are the mean and standard deviation for 600 s obtainedcomputing the RMS of the measurements per second at each windspeed.

The measured wind speed is affected by turbulence caused bythe blades because the anemometer used to collect wind speeddata is located behind the blades on the nacelle. Since themeasuredwind speed at the supervisory control and data acquisition (SCADA)system of the WinDS3000 turbine is inaccurate, wind speed was

Fig. 9. Motor torqueespeed curve.

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387 385

approximated from the angular velocity of the rotor measuredusing a tachometer that is installed in the rotor. Since the angularvelocity is constant at speeds higher than the rated speed, the windspeed cannot be converted from angular velocity at those timeinstants. So data measured in the power regulation region wasexcluded from the analysis at those time instants. Turbulent windcreated by the simulator had fluctuations of about 2 m/s(depending on the mean value), so the mean value and standarddeviation of the values measured at the WinDS3000 turbine werealso calculated using the same approach.

In Fig. 13, both the WinDS3000 turbine and the simulator haveincreasing vibration levels whenwind speeds increase. In addition,the standard deviation is reduced when the wind speed is gettingcloser to the ratedwind speed. It is considered that the variations invibrationmagnitudes are relatively low at high wind speed becausein those cases there is only a small change in the angular velocityand only the torque increases when the wind reaches the ratedspeed. In addition, the standard deviation is relatively large in theactual measurements because the appearance ratio with respect to

Fig. 10. Control logic for the pitch controller.

the wind speed is different and there may be large wind variationsover time intervals of 600 s duration. In contrast, the simulator wasoperated with constant turbulent intensity during experiments.

Fig. 14 shows the strain measured from August to September2011 at the blade root from the BHMIES installed in theWinDS3000turbine at the YeongHeungwind farm. Also, Fig. 14 shows the strainmeasured at the simulator blade root (Point 1 in Fig. 2). The strainfirst decreased and then increased gradually in the range of 5 m/s to7m/s. It is considered that the large rate of change at 5m/s and 6m/s is caused by large vibrations (caused by transient dynamics) whilethe generator starts operating at a cut-in wind speed of 4 m/s.Accordingly, the standard deviation is very large at 5 m/s for the

Fig. 11. 20 kW wind turbine simulator.

Fig. 12. Verification of the proposed control logic.

Fig. 13. Measured vibration trend at output of gearbox.

Fig. 14. Measured strains at the root of a blade.

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387386

actual measurements collected from the WinDS3000 turbine.There is a small strain during start-up because the blade moment issmall, but it increases in proportion to the moment and the powerincreases when the wind speed increases. The strain measured atthe WinDS3000 turbine shows a severe change because the actual

wind speed shows larger variations (similar to the measuredvibrations).

As described above, the vibrations at the gearbox and the strainsat the blade roots showed similar trends in both systems. Accord-ingly, the proposed simulator can be utilized for the development

K.-Y. Oh et al. / Renewable Energy 62 (2014) 379e387 387

of a fault detection algorithm and also can be applied for theverification of a condition monitoring system. However, the simu-lator has some limitations due to the underlying premise that thesimilarity does not apply at the component level but only at the(entire) system level. Thus, it is impossible to directly comparequantitative values measured from theWinDS3000 turbine and thesimulator at component level. In part, this is because there is nodetailed information regarding components for introducing simi-larity in Eqs. (2)e(4). The design of the gearbox and the blades inthe simulator also could not be exactly matched because thedetailed information on the WinDS3000 turbine is proprietary.Secondly, the blades of the simulator were designed as a sandwichfoam structure, while those of the WinDS3000 turbine have a boxbeam structure. To conclude, the quantitative comparison betweenthe actual wind turbine and the simulator does not provide anymeaningful information. In spite of these limits, quantitative valuesmeasured from simulator can be utilized to make statisticalapproach to make alarm level.

6. Conclusions

In this paper, a 20 kW wind turbine simulator was developedadopting a variable speedevariable pitch control strategy tosimulate various operating conditions of an actual wind turbine.The turbulent wind profile in the time domainwas generated usingthe wind power spectrum density, and a virtual wind turbinehaving rotor dynamic characteristics was realized by adopting aninertial filter. The torque curves for the motor and the generatorhaving similarity to the WinDS3000 wind turbine were estimatedand applied to the simulator. It was confirmed through testing thatthe proposed control logic operated correctly and stably. In addi-tion, it was observed that the simulator had similarities to an actualwind turbine by comparing vibration values and strain valuesmeasured at the gearbox and the blade root in the simulator withthe ones measured in the actual wind turbine. Condition moni-toring, fault diagnosis and life prediction algorithms are part offuture work and can be developed and tested using the acquisitionof signals from the simulator under various operating conditions. Inaddition, the simulator can be used for the development of variouscontrol algorithms for pitch controllers, inverters, and SCADAcontroller logic.

Acknowledgements

This work was supported by the New & Renewable EnergyProgram of the Korea Institute of Energy Technology Evaluation and

Planning (KETEP) grant funded by the Korea Government Ministryof Knowledge Economy. (No. 20113020020030).

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