protection of dfig wind turbine using fuzzy logic control · 2017-03-02 · original article...
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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: [email protected] (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.
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