IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 17, NO. 2, JUNE 2002 267

Propagation of Flicker in Electric Power NetworksDue to Wind Energy Conversions Systems

Carolina Vilar Moreno, Member, IEEE, Hortensia Amarís Duarte, Member, IEEE, andJulio Usaola Garcia, Member, IEEE

Abstract—Wind energy conversion systems (WECS) producefluctuating output power, which may cause voltage fluctuationsand flicker. Flicker assessment in networks may be difficultsince its evaluation requires long computing time and specialprocedures to calculate the flicker severity index, Pst. In thispaper, a frequency domain method to study flicker propagationis presented. This method is based on propagation of frequencycomponents from WECS output currents throughout the grid.In this way, a fast flicker analysis in a network of any size canbe performed. Also, an algorithm for flicker measurement inthe frequency domain, which allows Pst calculation, is proposed.Several study cases have been performed, and results have beencompared with time domain simulations, showing good agreementbetween them.

Index Terms—Flicker, power quality, wind turbines.

I. INTRODUCTION

T HE CONNECTION of wind turbines on the distributionnetwork may affect grid power quality. This paper inves-

tigates flicker propagation in a power network with fixed-speedwind power generators. A simple and accurate method in thefrequency domain to obtain the flicker severity parameter Pst inany point of a network is presented.

In continuous operation, wind turbines produce a nonconstanttorque during a revolution of the rotor due to different aerody-namic effects, such as tower shadow, yaw error, misalignment,and wind shear [1]. Those effects cause an oscillation of theaerodynamic torque up to 20% of the mean value at 3P fre-quency [2]. The torque oscillations produce power and voltagefluctuations at PCC, which increase with the turbulence inten-sity. Voltage flicker can become a limiting factor for connectingwind turbines at weak networks, and even on relatively strongnetworks with a great amount of wind power connected.

Measuring flicker at the point of common coupling (PCC)of a wind turbine for the assessment of its contribution to gridflicker level has the disadvantage of measuring not only the con-tribution of the flicker level produced by the wind turbine, butalso the flicker contribution caused by others producers and con-sumers connected to the same PCC. For this reason, the evalua-tion of the flicker emission of a wind turbine is based on currentor power measurements instead of voltage measurement.

Manuscript received April 4, 2000; revised December 6, 2001. This workwas supported in part by the Spanish Ministerio de Educación y Cultura underContract PB98-0034.

The authors are with the Universidad Carlos III de Madrid, Madrid, Spain(e-mail: cvilar@ing.uc3m.es; hamaris@ing.uc3m.es; jusaola@ing.uc3m.es).

Publisher Item Identifier S 0885-8969(02)05419-0.

II. V OLTAGE FLUCTUATIONS PRODUCED BYWIND TURBINES

The power available in the wind increases as the cube of thewind speed [1] according to

(1)

where is the density of the air, is the tip speed ratio, is theradius of the blade, is the angular velocity of the blade, andis the rotor swept area.

Power transmitted to the hub shafttakes into account themean wind speed incident at the hub, and it is modified bythe power coefficient . This power coefficient is dependenton the tip speed ratio and the blade pitch angle. In this paper,a wind model generates series of mean wind speeds and takesinto account the cycles of wind speed variation and its randomvariability.

Torque produced by a mean wind speedmay be obtainedas [1]

(2)

To this mean torque , the fluctuating components due totower shadow and wind shear effects must be superposed.

Tower shadow effect is produced because the wind turbinetower offers resistance to the wind flow, and it disturbs windflow both upstream and downstream. Far from the towerinfluence, wind speed is constant, while it increases whenapproaches the tower and decreases when coming closer.A Fourier series with harmonic multiples of 3P frequencycan represent this shadow effect [3]. Tower shadow is moreimportant for the wind turbines having their blades downwindof the tower.

The wind shear phenomenon also produces torque oscilla-tions caused by the wind speed gradient along the height of thearea swept by the blades. The wind speed gradient may be de-scribed in polar coordinates centred at the hub elevation by thebinomial series [3]. As the rotor samples the incoming wind,it sees the wind profiles as a periodical varying function of thetime with harmonics multiples of 3P.

Tower shadow and wind shear produce torque fluctuationsdeveloped on each blade, which are added to the mean torque.So torque produced by each bladecan be expressed as

(3)

0885-8969/02$17.00 © 2002 IEEE

268 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 17, NO. 2, JUNE 2002

where are the torque fluctuations produced by tower shadowand wind shear, is the torque produced by mechanical loadssuch as gravity acting on each blade, andis the torque due toyaw misalignment [4].

These torque fluctuations produce periodic fluctuations inthe output power from WECS. Inspecting power spectrumfrom a three-bladed turbine, it can be observed that periodiccomponents are concentrated on frequencies multiples ofthe blade passing frequency, mainly (3P, 6P …). Some otherperiodic power components, caused by different effects,may appear—for instance, drive-train oscillations or towerresonance. In Fig. 1, the power spectrum from a simulatedthree-bladed fixed-speed 350 kW, 34 rpm speed turn windturbine is shown.

Power fluctuations also have a stochastic component. In thisexample, wind turbine is at 90% rated power and turbulenceintensity is low: 3.5%. Real measurements show a similar powerspectrum to that shown in Fig. 1 [5].

III. FLICKER AND THE IEC FLICKERMETER

Flicker is defined as the impression of fluctuating brightnessor color, occurring when the frequency of observed variationlies between a few hertz and the fusion frequency of images ac-cording to the IEEE standard dictionary of electrical and elec-tronic terms (IEEE standard 100-1977). The flicker level is de-pendent on the amplitude of the voltage fluctuation, their fre-quency, and the shape of the waveform.

All types of voltage fluctuations may be assessed by directmeasurement using a Flickermeter, which complies with thespecification given in IEC-61 000-4-15 [6], IEC-868 [7]. Stan-dards IEC-61 000-3-3 [8] and IEC-61 000-3-7 [9] quantify theflicker perturbation.

In some cases, it is possible to know the spectral frequencydistribution of the flicker source and for this reason, it would bevery useful to have a Flickermeter model in the frequency do-main. Working in the frequency domain is very convenient notonly because of the computing time saving but also because thespectrum tells us which frequency component is the most im-portant for a flicker analysis. At the same time, it is possible todivide the signal spectrum in different bandwidths and, there-fore, a Flickermeter model in the frequency domain will allowobtaining the flicker contribution from different bandwidths.

Up to now, all the existing Flickermeters models and studiesin the frequency domain try to obtain the Instantaneous Flickerlevel by using several methods in the frequency domain[10]–[12]. From this value, and in order to obtain the Pstvalue, it is necessary to follow the IEC-recommendations [7].From the instantaneous Flicker level, in the time domain, thecumulative probability distribution is obtained and the Pstparameter can be deduced.

The Flickermeter proposed in this paper is different fromprevious researches; the key idea is to use a Pst-weightingfilter, which allows obtaining, directly, the Pst flicker value,with the only information of the power spectrum of the signal.The proposed Flickermeter solves some problems arisingfrom the traditional way (time domain) of obtaining Pst. For

Fig. 1. Power spectrum from a wind turbine.

Fig. 2. Flickermeter model in the frequency domain.

instance, when trying to assess Pst flicker values in a network,with a Flickermeter in the time domain, long simulations arerequired, which make these studies very cumbersome. Also, tocharacterize flicker emission from wind turbines according toIEC 61400-21 [13], a great amount of measured data shouldbe taken. Storage requirements may be reduced if spectralinformation were used.

A Flickermeter in the frequency domain, such as this pro-posed here, allows working only with the spectrum of the signal,which can be obtained in many ways, both in simulation studiesand in real measurements.

IV. FREQUENCYDOMAIN FLICKERMETER

One of the purposes of this paper is to present a direct methodto obtain the Pst by using a new digital Flickermeter in the fre-quency domain.

Several types of signal may be input to the proposed Flicker-meter to obtain the Pst flicker value Fig. 2:

• instantaneous voltage signal in the time domain (point 1)(modulated signal);

• root mean square (RMS) voltage signal in the time domain(point 2), (modulating signal);

• voltage spectrum (point 3).The process is explained next.

1) The modulated voltage signal (Point 1) is the input infor-mation to the Flickermeter.

2) In order to represent the internal behavior of an incandes-cent lamp, the voltage signal is squared.

3) Power spectrum is obtained by using a Fourier transformin the bandwidth of interest (0–35 Hz).

VILAR et al.: PROPAGATION OF FLICKER IN ELECTRIC POWER NETWORKS 269

4) The Pst flicker value of the signal may be calculatedby weighting the power spectrum with the Pst-weightingfilter , as (4)

Pst (4)

where is the amplitude of the frequency componentfi of the power spectrum, and represents the Pst-weighting factor for the frequency component.

Depending on the input type, all steps should be done consec-utively (as for example for signal point 1) or it would be possibleto go directly to step 3) (signal point 3).

It should be emphasized that the Pst flicker value might beobtained directly by (4), and it has not been necessary to obtainthe instantaneous Flicker level, to store the signal in a period oftime of 10 min, to divide this period of signal in classes, to obtainthe cumulative distribution probability function, and finally, toapply the Pst single formula proposed by the IEC [7]. The onlyinformation necessary is the power spectrum of the signal in thebandwidth of 0 to 35 Hz and the new Pst-weighting filterin the same bandwidth. A detailed study of the Pst-weightingfilter can be found in [14].

V. FLICKER PROPAGATION IN THEFREQUENCYDOMAIN

Power fluctuations produced by WECS could be written as atemporal expression like:

(5)

The index does not belong to a sequence. In general, thereare an infinite number of frequencies producing oscillationsaround the mean power. However, in WECS, as explainedpreviously, only a few components have an appreciable value,and they are well defined.

These power fluctuations, besides, depend on wind speed,wind turbulence and turbine characteristics. They do not seemto depend on network characteristics such as short circuit poweror X/R ratio.

Power fluctuations produce voltage and current fluctuations.Current fluctuations do not seem to depend on network char-acteristics, but voltage fluctuations do, mainly on short circuitpower and X/R ratio [15].

The expression of the current injected by the WECS in thegrid can be written as

(6)

This is the expression of a waveform modulated both in am-plitude and phase. From this expression, a spectrum can be de-rived immediately. This spectrum contains components of fre-quencies and .

If there are only a finite number of important frequencies, thecurrent injected by the WECS could be modeled as a set of cur-rent sources in parallel, Fig. 3. In this figure, two different kinds

Fig. 3. WECS modeled as a set of current sources.

Fig. 4. Three nodes network.

of sources can be observed. Conventional generators producepower only at fundamental frequency (50 or 60 Hz), while windenergy systems produce power, and therefore current, at an in-definite number of frequencies. The main components, however,are usually at well-defined frequencies. For each of these fre-quencies, the network can be modeled in the frequency domain,and the voltage in each node and for each frequency, can befound, if the injected current from wind generators are known.This can be done by a standard technique, such as nodal anal-ysis, taking into account that reactive elements vary with fre-quency.

With these results, superposition can be applied, and, there-fore, voltage spectrum can be found. This spectrum would be,as (5), that of a modulated signal, both in amplitude and phase.From this spectrum, flicker can be obtained by means of an IECflickermeter. Since we are dealing with spectra, a frequencydomain flickermeter is more convenient. Interaction betweenvoltage and frequency modulations has not been observed.

A program has been developed to calculate voltage valuesin the frequency domain. It can be used to model unbalancednetworks at any frequency and solve the network equations bynodal analysis.

VI. CASE STUDY

A. Example Case

This case consists of a wind farm connected to a three nodesnetwork (Fig. 4). Network parameters are given in Table I. Itis assumed that each turbine experiences the same wind andthat all wind turbines and generators have the same charac-teristics. Comparisons are made between time domain simula-

270 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 17, NO. 2, JUNE 2002

tions (performed with PSCAD/EMTDC) and frequency domaincalculations in order to investigate flicker emission level from30 three-blade stall regulated wind turbines (350 kW) with di-rect-coupled induction generators connected to the distributionnetwork. The output power of the Wind Farm is the total outputof the 30 individual wind turbines. Although this is not com-pletely correct for instantaneous power, it is good enough forthe purposes of this paper, because any correcting factor wouldlead to similar results. No wind turbulence has been considered.

Time domain simulation provides both the time current pro-duced by the wind farm and the time voltage variations at dif-ferent busbars due to wind farm power fluctuations for a deter-mined wind power generation level (83% of rated power thiscase) and system load level. Analyzing the voltage time seriesby the previously described Flickermeter, flicker levels at dif-ferent nodes are quantified by Pst (Table II).

A frequency analysis of the current produced by the windfarm reveals the most important frequencies present (Fig. 5).The current spectrum is limited to a finite number of signifi-cant frequencies for flicker study. So it is possible to specifythe current injected by WECS by a finite number of currentsources and to determine a voltage spectrum for each node.This voltage spectrum produces a similar flicker level to theone caused by time voltage series. The 50 Hz current compo-nent and the side-frequency components 3P and 6P located sym-metrically around the fundamental have only been consideredto study flicker. The last components appear, as shown earlier,mainly because of power fluctuations at the blade passing fre-quencies. The rest of the modulating frequencies have been ne-glected in order to simplify the method and also because 3P and6P components produce the most important modulation effecton 50 Hz voltage and therefore the most important effect onvoltage flicker.

As previously explained, current injected for each frequencyprovides the voltage component at that frequency for each node.Analyzing voltage spectrum for each node, the flicker effect onthe grid is determined. The predicted short-term flicker param-eters, Pst, obtained are shown in Table II. A comparison hasbeen made with previous results obtained from voltage time se-ries analysis.

Table II shows that flicker levels caused by time voltage se-ries are quite similar to flicker levels obtained by current prop-agation in the frequency domain analysis, in spite of having ne-glected frequency components. It can be observed that propa-gation with considered frequencies are enough to predict totalflicker level at different busbars of the power system. In this way,network flicker levels expected from a wind turbine or groupof wind turbines at a specific location, can be accurately pre-dicted with no need of voltage measurements on every node.This method provides a simple way to quantify flicker severityat a power system with WECS knowing time current series in-jected by wind turbines connected to the network. This currentcould be either time simulated or measured.

Results in Table II show that node 1 has the highest flickerlevel in spite of the fact that node 2 seems to be more critical,since the wind farm is closer and the fault level is lower. Thesame thing could be said for node 3. However, the X/R ratioat nodes 2 and 3 is similar to what in previous works has beenconsidered the point of minimum flicker [16].

TABLE ITHREE NODESNETWORK PARAMETERS

TABLE IINETWORK FLICKER LEVELS

Fig. 5. Current spectrum injected by WECS in node 2.

Fig. 6. Complex network.

B. Application Case

The previous case has shown the method to evaluate flicker.In this application case, the propagation method is used to ana-lyze the effects of WECS in a complex network. A distributionnetwork with eight nodes has been studied in the frequency do-main only, in order to assess flicker in all nodes for differentwind power penetration levels. The power system is shown inFig. 6.

VILAR et al.: PROPAGATION OF FLICKER IN ELECTRIC POWER NETWORKS 271

TABLE IIINETWORK FLICKER LEVELS

TABLE IVFLICKER STUDY CASES FOR THECOMPLEX NETWORK

Different possibilities are going to be considered throughoutthis application case.

In the first study case, a wind farm is connected to the networkat node 7 by a 45/0.69 kV transformer. The wind farm is madeup of 30 fixed-speed stall regulated wind turbines.

Assuming that wind speed varies in time in such a way thatthe wind farm operates at a mean power of 83% rated power(Case I) and 100% rated power (Case II), line current producedby the wind farm is obtained for each case by time simulationswith PSCAD/EMTDC. By propagation method and frequencydomain analysis, wind farm contribution to voltage flicker isdetermined. Results are shown in Table III. A power increaseof nearly 20% produce higher flicker levels as expected. Bothcases show that the worst busbar is the one where the wind farmis connected.

A second analysis is performed when an identical wind farmis also connected at node 5 of the power system. Different windpower generation levels and also different system load levelshave been considered. Current inputs for each wind farm are ob-tained for different cases from time domain simulations. Then,the propagation technique is applied.

Table IV shows that Case V, where both wind farms are op-erating at their rated power, is the worst case. However, theirflicker effect is not critical to the network because the wind

TABLE VCOMPLEX NETWORK CHARACTERISTICS

farms are connected to points with a high ratio of fault level torated power (Table V). From flicker propagation, it is observedthat higher flicker values appear at nodes 3, 4, and 5. This is dueto the lower fault level at these points. This can also be observedin Cases III and IV. As expected, flicker level gets higher valueswhen wind power generation level increases.

Interaction between currents injected has been observed whena second farm is connected to the power system. This means thatsuperposition of currents from different wind farms cannot beconsidered without an interaction model.

In order to determine the effect of system load level on flicker,some changes on the system load have been done. Consideringthe system in Case II with a reduction of 50% and with 10%of initial system load, simulations have shown there is a smallinfluence on network flicker level due to changes on static loads[17]. Just a maximum increase of 2.5% the former and 4.5% thelatter on network flicker level.

VII. CONCLUSIONS

A method for flicker analysis in the frequency domain hasbeen described. It is especially suitable for flicker analysis withwell-defined modulation components.

It provides a simple tool for the assessment of the incidenceof WECS on power quality.

Results have been compared with time-domain simulations,and a good agreement has been found between both methods.

Some examples have been made in order to show the capabil-ities of the method. This could be easily used for flicker propa-gation analysis in industrial networks.

REFERENCES

[1] L. L. Freris,Wind Energy Conversions Systems. London, U.K.: Pren-tice-Hall, 1990.

[2] G. Gerdes and F. Santjer, “Power quality of wind turbines and theirinteraction with the grid,” inProc. 1994 Eur. Wind Energy Conf., pp.1112–1115.

[3] D. A. Spera,Wind Turbine Technology. New York: ASME Press, 1995.[4] H. Amarís, C. Vilar, J. Usaola, and J. L. Rodríguez, “Modeling wind en-

ergy conversions systems in the frequency domain for flicker analysis,”Wind Eng., vol. 23, no. 4, pp. 189–203, 1999.

[5] T. Thiringer, “Power quality measurements performed on a low-voltagegrid equipped with two wind turbines,”IEEE Trans. Energy Conv., vol.11, pp. 601–606, Sept. 1996.

[6] IEC 61 000-4-15, “Electromagnetic Compatibility (EMC). Part 4:Testing and measurements techniques—Section 15: Flickermeter.Functional and design specifications,” Bureau Central CommissionElectrotech. Int., Geneva, Switzerland, Nov. 1997.

[7] IEC/TR2 80 868-0, “Flickermeter—Part 0. Evaluation of flickerseverity,”, 1991.

[8] IEC 61 000-3-3, “Electromagnetic Compatibility (EMC). Part 3:Limits—Section 3: Limitation of voltage fluctuations and flicker inlow-voltage supply systems for equipment with rated current 16 A,”Bureau Central Commission Electrotech. Int., Geneva, Switzerland,1994.

272 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 17, NO. 2, JUNE 2002

[9] IEC 61 000-3-7, “Electromagnetic Compatibility (EMC). Part 3:Limits—Section 7 Assessment of emission limits for fluctuatingloads in MV and HV power systems,” Bureau Central CommissionElectrotech. Int., Geneva, Switzerland, 1996.

[10] K. Srinivasan, “Digital measurement of voltage flicker,”IEEE Trans.Power Delivery, vol. 6, pp. 1593–1598, Apr. 1991.

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[12] J. Arrillaga, N. R. Watson, and S. Chen,Power System Quality Assess-ment. New York: Wiley, 2000.

[13] IEC 61 400-21, “Ed. 1. Wind turbine generator systems. Part 21: Mea-surement and assessment of power quality characteristics of grid con-nected wind turbines,”, 2001.

[14] H. Amarís and J. Usaola, “A new Pst-weighting filter for the flickermeterin the frequency domain,”Eur. Trans. Elect. Power, vol. 11, pp. 23–30,Jan./Feb. 2001.

[15] J. Usaola, C. Vilar, H. Amarís, P. Ledesma, and J. L. Rodríguez, “Char-acterization of WECS through power spectra for power quality studies,”in Proc. 1999 Eur. Wind Energy Conf., pp. 766–769.

[16] P. Sørensen, J. O. Tande, L. M. Søndergaard, and J. D. Kledal, “Flickeremission levels from wind turbines,”Wind Eng., vol. 20, no. 1, pp.39–46, 1996.

[17] E. Bossanyi, Z. Saad-Saoud, and N. Jenkins, “Prediction of flicker pro-duced by wind turbines,”Wind Energy, vol. 1, pp. 35–51, 1998.

Carolina Vilar Moreno (M’02) was born in Madrid,Spain, on May 27, 1974. She received the B.S. degreein electromechanical engineering from UniversidadCarlos III, Madrid, Spain, where she is currently pur-suing the Ph.D. degree in electrical engineering.

She is currently with the Department of ElectricalEngineering, Universidad Carlos III. Her research in-terests are wind energy systems and power quality.

Hortensia Amarís Duarte (M’01) received the B.S.and Ph.D. degrees in electrical engineering fromE.T.S. Ingenieros Industriales (ETSIIM), Madrid,Spain, in 1990 and 1995, respectively.

In 1990, she joined the Department of AutomaticControl, ETSIIM, until 1995. She is presently anAssociated Professor in the Department of ElectricalEngineering, Universidad Carlos III, Madrid. Herresearch interests include power quality and windenergy systems.

Julio Usaola García (M’01) received the B.S. de-gree and Ph.D. degrees in electrical engineering fromE.T.S. de Ingenieros Industriales (ETSIIM), Madrid,Spain, in 1986 and 1990, respectively.

In 1988, he joined the Department of ElectricalEngineering, ETSIIM, where he remained until1994. He is presently an Associated Professor withthe Department of Electrical Engineering, Uni-versidad Carlos III, Madrid. His research interestsinclude power quality and wind energy systems.