Alexandria Engineering Journal (2016) 55, 941–949

HO ST E D BY

Alexandria University

Alexandria Engineering Journal

www.elsevier.com/locate/aejwww.sciencedirect.com

ORIGINAL ARTICLE

Protection of DFIG wind turbine using fuzzy logic

control

* Corresponding author.

E-mail address: m_m_ismail@yahoo.com (M.M. Ismail).

Peer review under responsibility of Faculty of Engineering, Alexandria

University.

http://dx.doi.org/10.1016/j.aej.2016.02.0221110-0168 2016 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Mohamed M. Ismail *, Ahmed F. Bendary

Dep of Electrical Power and Machines Faculty of Engineering, Helwan University Cairo, Egypt

Received 17 December 2015; revised 7 February 2016; accepted 20 February 2016

Available online 25 March 2016

KEYWORDS

Double Fed Induction Gen-

erator;

Crowbar protection;

ANFIS;

Fuzzy logic;

Fault current and voltages

Abstract In the last 15 years, Double Fed Induction Generator (DFIG) had been widely used as a

wind turbine generator, due its various advantages especially low generation cost so it becomes the

most important and promising sources of renewable energy. This work focuses on studying of using

DFIG as a wind turbine connected to a grid subjected to various types of fault. Crowbar is a kind of

protection used for wind turbine generator protection. ANFIS controller is used for protection of

DFIG during faults. The fault current under symmetric and asymmetric fault is presented as well as

a way to control the increase in rotor current which leads to voltage increase in DC link between

wind generator and the grid. ANFIS is used for solving such problem as it is one of the most com-

monly AI used techniques. Also the current response of DFIG during fault is improved by adapting

the parameters of PI controllers of the voltage regulator using fuzzy logics. ANFIS also in this

paper is used for detecting and clearing the short circuit on the DC capacitor link during the oper-

ation. A simulation study is illustrated using MATLAB/Simulink depending on currents and volt-

ages measurement only for online detection of the faults. The proposed technique shows promising

results using the simulation model. 2016 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an

open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Wind power generation industry has become widely used inthe last few years and takes more attention of manufactures.There are many reasons for adding more wind energy to the

electric networks. For instance, wind generation is supportedby not only being clean and renewable but also having minimalrunning cost requirements [1].

Variable speed wind turbine topologies include many differ-

ent generator/converter configurations, based on cost, effi-ciency, annual energy capturing, and control complexity ofthe overall system [2].

Due to the fast enhancement and development in manufac-

ture of power electronic converter technology as well as thedevelopment of induction machines specially Double FedInduction Generators and its advantages of small capacity of

converters, high energy and flexible power control [3], DFIGhas been widely used for large-scale wind power generationsystems due to its various advantages, such as variable speed

operation, controllable power factor and improved system effi-ciency [4]. The amount of energy extracted from the winddepends not only on the incident wind speed, but also on the

Figure 1 Wind turbine connected to grid.

942 M.M. Ismail, A.F. Bendary

control system applied on the wind energy conversion system.The DFIG is equipped with a back-to-back power electronic

converter, which can adjust the generator speed with the vari-ety of wind speed. The converter is connected to the rotorwindings, which acts as AC excitation system. Wind turbines

began to contribute and increase steadily in electric power gen-eration production in electric networks.

The power electronic converter between the stator and thegrid as in variable speed wind turbine can determine the short

circuit fault current easily, in DFIG in either constant speed orvariable speed wind turbines and the converter is connectedbetween the rotor winding and the grid and exposed to high

current during fault occurrence [5].

Figure 2 Construction of DFIG.

The contribution of DFIG wind turbine in the fault currentmust be taken into account as the wind farm must stay con-

nected to the grid during system disturbances in order to sup-port the network voltage and frequency. Some precautionmust be taken into account to ensure the safe operation of

the rotor side inverter of the DFIG, as the rotor current willincrease during grid failures. Therefore, DFIG requires a pro-tection system named active crowbar disconnects the converterin order to protect it turning the generator into a squirrel cage

induction machine [6,7]. Till now the crowbar protection stilltakes the attention of many researchers trying to study itseffects for both the grid and the wind turbine, and also many

papers have studied only the effect of fault on the DFIG [8–10], as well as many types of crowbar protections had beenintroduced which divide the crowbar protection system into

three types, conventional crowbar, series crowbar and a newprotection method, named the outer crowbar, as this type

Figure 3 Equivalent circuit of DFIG.

Figure 4 Locations of faults on the grid.

Protection of DFIG wind turbine 943

was presented in [11] similar to the series crowbar but the maindifference between the series crowbar and the outer series

crowbar is the outer crowbar connects in series with the DFIGinstead of stator windings only. According to the position ofcrowbar resistance with respect to either stator winding or

rotor winding side [12], the amount and the direction of powerflowing through the rotor circuit depend on the operatingpoint of the induction machine.

Two control schemes are developed for rotor- and grid-side

converters. Shunt capacitor is employed as a dc link betweenthe two converters. Ref. [13] shows the behavior of DFIG incase of capacitor failure. However, none of the previous work

introduces a solution for this problem. Ref. [14] presents differ-ent fault conditions such as line to ground faults, line to linefaults, double line to ground faults and three phase symmetric

faults using genetic algorithm based fuzzy controller is incor-porated into the Doubly fed Induction Generator (DFIG)Wind Energy Conversion System. While the behaviors ofDFIG wind turbine under micro-interruption fault are studied

in [15], the micro-interruption is a disconnection of the electricgrid for a short moment where a control strategy of the UnifiedPower Flow Control (UPFC) using PI controller is presented.

Different kinds of research are applied on the crowbar of theDFIG as indicated in [17–22] but none of them used ANFIS

for DFIG protection. ANFIS controller was implementedfor MPPT of DFIG [16].

This paper presents a new technique of crowbar implemen-tation using ANFIS controller. The response of DFIG duringdifferent types of faults is improved by adapting the parame-

ters of PI controllers of the voltage regulator using fuzzy log-ics. ANFIS is also used for detecting and clearing the shortcircuit on the DC capacitor link during the operation. Thedynamics of the system and control actions are simulated with

detailed model using MATLAB/SIMULINK. This paper isdivided into six sections. First section is an introduction; sec-ond section is representing the model used in Simulink. The

equations of DFIG are studied in Section3 3, Section 4 showsthe fuzzy logic algorithm, Section 5 is the simulation resultsand finally Section 6 represents the conclusion.

2. System under study

The studied system consists of DFIG (575 V, 10 MW) con-nected to a grid (2500 MVA) through a transmission line sys-tem of 30 km, 25 kV, step-up and down transformers as shownin Fig. 1. The representation of such networks containing

DFIG working as a wind turbine is done through representing

Figure 5 ANFIS technique for crowbar protection.

944 M.M. Ismail, A.F. Bendary

the mathematical equations that describe the dynamic method-ology of wind energy conversion to electrical energy. Windenergy conversion system consists of two main parts: wind tur-bine with the pitch regulator and the induction generator each

can be described as follows:

2.1. Wind turbine with regulator pitch model

The modeling of this module is depending on the representa-

tion of the aerodynamic power captured from the wind andhow much wind energy is converted to mechanical energy, so

the mathematical equations representing it will relate to theoutput power directly to the air flow as follows [7]:

Pw ¼ 1

2qCpðk; bÞARv

3w ð1Þ

Cpðk; bÞ ¼ 0:22ð116=ki 0:4b 5:0Þe12:5ki ð2Þ

ki ¼ 1

kþ 0:08b 0:035

b3 þ 1

1

ð3Þ

where Pw is the aerodynamic power captured from wind; q isthe density of air kg/m3; AR is the area that the wind powercan be obtained; Cp is the power coefficient; vw is the windspeed; k is the tip speed ratio (TSR); ki is the middle variable;

and b is the pitch angle. The main construction of DFIGmodel is indicated in Fig. 2.

3. Induction generator model

For the mechanical model, it was concentrated only on theparts of the dynamic structure of the wind turbine that con-tributes to the interaction with the grid [8]. Therefore, only

the drive train is considered, while the other parts of the windturbine structure, e.g. tower and flap bending modes, areneglected. When modeling the drive train, it is a common prac-

tice to neglect the dynamic, of the mechanical parts, as theirresponses are considerably slow in comparison with the fastelectrical ones, especially for machines with great inertia. The

rotational system may therefore be modeled by a single equa-tion of motion:

JWG

dwr

dt¼ TW TG Dwr ð4Þ

where JWG is the wind turbine mechanical inertia plus genera-tor mechanical inertia [kg m2], Tw is mechanical torque. TG isgenerator electromagnetic torque [N m], and Distraction coef-

ficient [N m/rad].Also the equivalent circuit of DFIG model is shown in

Fig. 3.

Figure 6 Implementation of ANFIS for capacitor short circuit.

Figure 7 ANFIS inputs/outputs for crowbar protection.

Protection of DFIG wind turbine 945

This model can be described by the following space vector

equations in synchronous coordinates as follows:

usd ¼ Rsisd þ d

dtWsd x1Wsq ð5Þ

usq ¼ Rsisq þ d

dtwsq þ x1wsd ð6Þ

urd ¼ Rrird þ d

dtwrd x1wrq ð7Þ

urq ¼ Rrirq þ d

dtwrq þ x1wrd ð8Þ

Wsd ¼ Lsisd þ Lmird ð9ÞWsq ¼ Lsisq þ Lmirq ð10ÞWrd ¼ Lmisd þ Lrird ð11ÞWsq ¼ Lmisq þ Lrirq ð12Þ

where Rs, Rr are stator, rotor resistance, LS, Lr are stator,rotor leakage inductance, Lm is magnetizing inductance, ls,lr are stator, rotor voltage, and is, Ir are stator rotor Current

[10]. W1 is synchronous angular speed. When the d referenceframe synchronous coordinate was oriented along with the sta-tor flux of the DFIG,

The active and reactive power equations at the stator androtor windings are written as follows:

Ps ¼ Vds idsþ Vqs iqs ð13ÞQs ¼ Vqs ids Vds iqs ð14ÞPr ¼ Vqr idrþ Vqr iqr ð15ÞQr ¼ Vdr idr Vdr iqr ð16Þ

The electromagnetic torque is expressed as follows:

Tem ¼ 3

2 p2ðuds iqs uqsidsÞ ð17Þ

Figure 8 Adaptation of PI controller using fuzzy logic.

Figure 9 Membership functions of inputs (e, De).

Figure 10 Membership functions of outputs (KP1, KI1).

946 M.M. Ismail, A.F. Bendary

4. Fuzzy logic control algorithm

The use of fuzzy logic control (FLC) has become popular overthe last decade because it can deal with imprecise inputs, doesnot need an accurate mathematical model and can handle non-linearity. Microcontrollers have also helped in the populariza-

tion of FLC. Neural networks are the systems that getinspiration from biological neuron systems and mathematical

theories for learning. They are characterized by their learning

ability with a parallel-distributed structure [15].An Adaptive Neuro-Fuzzy Inference System (ANFIS) is a

cross between an Artificial Neural Network (ANN) and a

fuzzy inference system (FIS) as the main purpose of usingthe Neuro-Fuzzy approach is to automatically realize the fuzzysystem by using the neural network methods. An adaptive net-work is a multi-layer feed-forward network in which each node

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-1

-0.5

0

0.5

1

1.5

2

2.5

time (sec)

AN

FIS

out

put

Figure 11 ANFIS output during grid faults and DC capacitor

shorted.

0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26

200

400

600

800

1000

1200

time (sec)

Vdc

dur

ing

capa

cito

r sho

rt ci

rcui

t

Figure 12 Vdc during capacitor short circuit.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

1200

1250

1300

1350

1400

time (sec)

Vdc

(vol

t)

with ANFIS crowbar without crowbar

Figure 13 Vdc with and without crowbar during faults.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

time (sec)

stat

or c

urre

nt (p

.u)

with ANFIS crowbar without crowbar

Figure 14 Stator current with and without crowbar during

faults.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 21140

1160

1180

1200

1220

1240

1260

1280

1300

1320

1340

time (sec)

Vdc

Conventional PI with PI fuzzy adaptation

Figure 15 Vdc with and without PI adaptation during faults.

0.4 0.6 0.8 1 1.2 1.4 1.6 1.80.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

time (sec)

stat

or c

urre

nt o

f DFI

G (P

.U)

Conventional PI with PI fuzzy adaptation

Figure 16 Stator current with and without PI adaptation during

faults.

Protection of DFIG wind turbine 947

(neuron) performs a particular function on incoming signals.The form of the node functions may vary from node to node.

In an adaptive network, there are two types of nodes, adaptiveand fixed.

In this paper a novel contribution through using ANFIS isachieved to reach two main goals, first to detect and protect

the rotor winding of wind turbine generator when subjectedto fault. Second to implement a standby DC capacitor in oper-

ation in case of the original capacitor is damaged due to volt-age rise on it during fault subjection. Also fuzzy logiccontroller is used for PI tuning adaption of controller param-

eter to achieve better response.

948 M.M. Ismail, A.F. Bendary

The implementation of crowbar using ANFIS technique forthe wind turbine during grid faults is presented. The fault loca-tions on the grid are indicated in Fig. 4.

4.1. ANFIS design for crowbar

The main purpose of using ANFIS controller in this paper was

for implementation of crowbar resistance in the rotor side ofwind turbine during grid fault. ANFIS technique is shown inFig. 5, Fig. 6 shows the standby capacitor implementation during

operation using ANFIS technique. The ANFIS controller struc-ture is shown in Fig. 7, and the outputs of the ANFIS controllerhave three values: Zero to indicate no fault occurrence, one for

fault occurrence, and two for capacitor short circuit.The inputs of ANFIS controller are as follows:

1. VDC

2. Stator current Is = sqrt(Isa2 + Isb

2 + Isc2)

3. Stator voltage Vs = sqrt(Vsa2 + Vsb

2 + Vsc2)

The fuzzy logic observer parameters are turned using neuralnetwork method which is well known in MATLAB programas ANFIS structure. The parameters are selected such that, opti-

mization method is hybrid, the membership function is gbellmf,the membership function output is linear, error tolerance waschosen to be 0.01, the number of epochs is 1000, grid partitions,the inputs of the grid partitions are the number MFS are 3, MF

type is gbellmf, the outputs is MF type defined to be constant.

4.2. PI adaptation using fuzzy logic controller

This paper proposed two inputs-two outputs self-tuning of aPI controller. The controller design used the error and changeof error as inputs to the self-tuning, and the gains (KP1;KI1) as

outputs. The FLC is adding to the conventional PID controllerto adjust the parameters of the PI controller online accordingto the change of the signals error and change of the error. The

proposed controller also contains a scaling gains inputs (ek,Dek) as shown in Fig. 8, to satisfy the operational ranges(the universe of discourse) making them more general.

Now the control action of the PID controller after self-

tuning can be described as follows:

UPID ¼ KP2 eðtÞ þ KI2

Zedt ð18Þ

where KP2;KI2, are the new gains of PI controller and are equal

to the following:

KP2 ¼ KP1 KP; KI2 ¼ KI1 KI ð19ÞwhereKP1 andKI1, are the gains outputs of fuzzy control, that

are varying online with the output of the system under control.

And KP and KI, are the initial values of the conventional PID.For the system under study the universe of discourse for

both e(t) and De(t) may be normalized from [1,1], and thelinguistic labels are Negative Big, Negative, medium, Nega-

tive small, Zero, Positive small, Positive medium, PositiveBig, and are referred to in the rules bases as NB,NM,NS,ZE,PS,PM,PB, and the linguistic labels of the outputs are

Zero, Medium small, Small, Medium, Big, Medium big, verybig and referred to in the rules bases as Z,MS,S,M,B,MB,VB as shown in Figs. 9 and 10.

5. Simulations results

The simulations are done using the model shown in Fig. 4through subjecting the system to different faults in different

places. The first fault is a triple line to ground fault at the windturbine side occurring during the first duration (0.2–0.4 s), sec-ond fault is double line to ground fault at the loads during the

second duration (0.6–0.8 s), and third fault located at the plant(2 MVA) is triple line to ground fault occurring during thethird duration (1–1.2 s). Finally a single line to ground faultoccurs before transformer (47 MVA) during the fourth dura-

tion (1.4–1.9 s). The DC capacitor link is also shorted duringthe fifth duration (0.12–0.17 s). Fig. 11 presents the ANFISoutput during grid faults from the 1st to 5th duration indicat-

ing by 1 or in other word a fault is detected and then clearedand returned back to its normal operation (0), and also theDC capacitor short circuit at the 5th duration at which the

ANFIS output gives value two and the fault is cleared andreturns back to its normal value.

Fig. 12 shows the change of the DC capacitor voltage dur-

ing capacitor short circuit fault beginning with nominal value1200 V and decreasing to approximate zero value and thereturning back to its normal value after clearing the fault.

Fig. 13 shows the rise in Vdc without using the crowbar pro-

tection and how this value is reduced when using ANFIS crow-bar, and also Fig. 14 shows the high oscillation and overshoots appear in the stator current in case of not using ANFIS

controller and how this control algorithm succeeds in dampingthe output signal to reach its nominal value in a few seconds.

Figs. 15 and 16 represent the enhancement achieved for Vdc

and the stator current in reducing the overshoots and oscilla-tion damping due to implementation of PI adaptation usingfuzzy logic controller compared to the conventional PI.

6. Conclusion

The short circuit current fault analysis in DFIG wind turbine

connected to a grid still takes the attention of many research-ers. This paper succeeds in studying its effect when subjected tovarious types of faults for both the grid and the wind turbinealso in designing such controller based on AI optimization

technique which shows greet advance in controller output asshown in simulation results.

This paper also presents a new technique of crowbar imple-

mentation using ANFIS controller as well as the response ofDFIG during different types of faults is improved by adaptingthe parameters of PI controllers of the voltage regulator using

fuzzy logic controller.Finally the ANFIS optimization technique is succeeded in

detecting and clearing the failure of the DC capacitor link dur-ing operation.

References

[1] Ali H. Kasem Alaboudy, Ahmed A. Daoud, Sobhy S. Desouky,

Ahmed A. Salem, Converter controls and flicker study of

PMSG-based grid connected wind turbines, Ain Shams Eng. J. 4

(1) (2013) 75–91.

[2] M. Chinchilia, S. Arnaltes, J. Burgos, Control of permanent-

magnet generator applied to variable-speed wind-energy systems

connected to the grid, IEEE Trans. Energy Convers. 21 (1) (2006).

Protection of DFIG wind turbine 949

[3] S. Muller, M. Deicke, R.W. De Doncker, Doubly fed induction

generator systems for wind turbines, IEEE Ind. Appl. Mag. 8 (3)

(2002) 26–33.

[4] K. Tan, S. Islam, Optimum control strategies in energy

conversion of PMSG wind turbine system without mechanical

sensors, IEEE Trans. Energy Convers. 19 (2) (2004).

[5] Ahmed A. Daoud, Sobhy S. Dessouky, Ahmed A. Salem,

Control scheme of PMSG based wind turbine’ for utility

network connection, in: 10th EEEIC Proc., Rome, Italy, May

8–10, 2011.

[6] Anca D. Hansen, Florin Iov, Poul Sørensen, Frede Blaabjerg,

Overall control strategy of variable speed doubly-fed induction

generator wind turbine, in: Nordic Wind Power Conference, 1–2

March 2004.

[7] S. Heier, Grid Integration of Wind Energy Conversion Systems,

John Wiley and Sons Ltd, 1998, ISBN: 0-471-97143-X.

[8] D.W. Novotny, A. Lipo, Vector Control and Dynamics of AC

Drives, Clarendon Press, 1996.

[9] F. Mei, B. Pal, Modal analysis of grid-connected doubly fed

induction generators, IEEE Trans. Energy Convers. 22 (3)

(2007) 728–736.

[10] L. Xu, Y. Wang, Dynamic modeling and control of DFIG –

based wind turbines under unbalanced network conditions,

IEEE Trans. Power Syst. 22 (1) (2007) 314–323.

[11] Mohamed Ebeed, Omar NourEldeen, A.A. Ebrahim, Assessing

behavior of the outer crowbar protection with the DFIG during

grid fault, in: 2nd International Conference on Energy Systems

and Technologies, Cairo, Egypt, 18–21 February, 2013.

[12] A.H. Kasem Alaboudy, Power Conditioning and Performance

Enhancement of Wind Based Distributed Generation, PhD

thesis, Minia University, Egypt, 2009.

[13] Ion Boldea, ‘‘Variable Speed Generators” (Electric Power

Engineering Series), CRC, 2005.

[14] B. Babypriya, N. Devarajan, Simulation and analysis of a DFIG

wind energy conversion system with genetic fuzzy controller, Int.

J. Soft Comput. Eng. (IJSCE) 2 (2) (2012).

[15] M. Ali Dami, K. Jemli, M. Jemli, M. Gossa, Doubly fed

induction generator, with crow-bar system under micro-

interruptions fault, Int. J. Electr. Eng. Infor. 2 (3) (2010).

[16] Ravinder K. Kharb, Md. Fahim Ansari, S.L. Shimi, Design and

implementation of ANFIS based MPPT scheme with open loop

boost converter for solar PV module, Int. J. Adv. Res. Electr.

Electron. Instrum. Eng. 3 (1) (2014) 2320–3765, ISSN (Print).

[17] Nagendra Singh, Prasenjit Basak, Analysis of dynamic behavior

of a DFIG based wind turbine during symmetrical three phase

fault at grid end using crowbar protection, Int. J. Adv. Res.

Electr. Electron. Instrum. Eng. 4 (7) (2015).

[18] Patel Poonam Jayantilal, Hardik H. Raval, Ashish R. Patel,

Evaluation & performance analysis of DFIG wind turbine with

crowbar protection under short circuit & voltage DIP condition,

Int. J. Technol. Res. Eng. 2 (8) (2015).

[19] S. Divya, T. Krishnakumari, Review of control strategies for

DFIG wind turbine to enhance LVRT capability, IJISET – Int.

J. Innovative Sci. Eng. Technol. 2 (4) (2015).

[20] A. Safaei, S.H. Hosseinian, H.A. Abyaneh, N. Fallah-Amini,

Investigation of SFCL impacts on crowbar protection of DFIG

based wind turbine, in: Electrical Power Distribution Networks

Conference (EPDC), 2015, 20th Conference on 28–29 April 2015.

[21] Beshoy Abdou Aziz, Maged N.F. Nashed, Salah G. Ramdan,

Tarek A. Dkrori, Improvement FRT capability of DFIG wind

energy based on three level converter, Int. J. Curr. Eng. Technol.

5 (4) (2015).

[22] Yunqi Wang, B.T. Phung, Jayashri Ravishankar, Fault analysis

of an islanded micro-grid with doubly fed induction generator

based wind turbine, in: CIRED, 23rd International Conference

on Electricity Distribution, Lyon, 15–18 June 2015.