a fuzzy control strategy for power smoothing and grid dynamic response enrichment of a...

WIND ENERGY

Wind Energ. (2013)

Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/we.1637

RESEARCH ARTICLE

A fuzzy control strategy for power smoothing andgrid dynamic response enrichment of agrid-connected wind energy conversion systemAbdul Motin Howlader1, Naomitsu Urasaki1, Alok Pratap1, Tomonobu Senjyu1 andAhmed Yousuf Saber2

1 Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa903-0213, Japan

2Operation Technology, Inc., California, USA

ABSTRACT

This paper concentrates on the output power smoothing and the grid dynamic response enhancement of a grid-interactiveMW-class permanent magnet synchronous generator-based wind energy conversion system (WECS). A simple fuzzy con-troller method is applied to improve the overall performance of the WECS. The proposed method can retrieve the storingkinetic energy from the inertia of a wind turbine, perfectly. As a result, it can ensure a proficient power smoothing ofthe variable speed WECS. On the other hand, the grid side inverter is controlled by the fuzzy controller. This approachcan reduce the fluctuation of DC link voltage and can deliver a smooth power to the power grid. The proposed methodis compared with two other methods such as the maximum power point tracking control method and the without fuzzycontroller method. A simple shunt circuit also includes in the DC link circuit. Therefore, during the system fault condition,the WECS can perform a stable operation. Effectiveness of the proposed method is verified by numerical simulations.Copyright 2013 John Wiley & Sons, Ltd.

KEYWORDS

wind turbine; power smoothing; kinetic energy; maximum power point tracking; fuzzy controller; permanent magnet synchronousgenerator

Correspondence

Tomonobu Senjyu, Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho,Nakagami, Okinawa 903-0213, Japan.E-mail: [email protected]

Received 9 October 2011; Revised 20 November 2012; Accepted 21 April 2013

1. INTRODUCTION

In recent years, the penetration of the wind power into electric grids has been enlarged. Wind energy is one of the rapidgrowing industries among preferred sources (e.g., solar, wave, hydro and biomass) of the green energy.1 It can meet theelectricity demand with high reliability, economically and environmental friendly. Depending on the rotational speed, thereare two types of wind energy conversion systems (WECSs): (i) fixed speed wind turbine, and (ii) variable speed wind tur-bine (VSWT). Due to the variation of wind velocity, the VSWT is the most popular WECS because it can utilize the windenergy efficiently.2 VSWT systems are based on doubly-fed induction generators (DFIGs), permanent magnet synchronousgenerators (PMSGs) and synchronous generators with electromagnets. The PMSG-based VSWT has been increased owingto the reliability, availability, simple structure and high energy efficiency. Moreover, the PMSG can operate without a gear-box. Hence, it can release from all difficulties of a gear box. Usually, the PMSG-based WECS is connected to the powergrid via an ACDCAC converter system. In this system, the WECS is not required to synchronize with the rotationalspeed of the grid frequency.

However, wind velocity is inconstant, and the wind turbine output power is proportional to the cube of wind speed.Thus, the output power of the WECS is fluctuated, which incurs the frequency deviation from the rated value.35Various methods have been proposed to generate a smooth output power of the WECS such as ultra capacitors,68

Copyright 2013 John Wiley & Sons, Ltd.

Fuzzy control strategy for power smoothing and grid control A. M. Howlader et al.

super conducting magnetic energy storage,9,10 flywheel energy storage system,11,12 storage batteries,13 fuel cell,14and backup generator15 and so on. But, installation and maintenance costs of these active devices are extremelyhigh. Therefore, several researches regarding to the output power smoothing have been published by using of thepitch angle control or the inertia of VSWTs.1622 Extra energy storages or devices are not required for these powersmoothing techniques.

In the works of Sato and Saitoh and Abedini and Nasiri16,17, output power smoothing methods have been proposedby using the rotor inertia. Output power fluctuations of the VSWT can be smoothed at wind speed variations; how-ever, detail characteristics of the power smoothing and the power efficiency of a VSWT are not addressed. Therefore,large wind speed variations may put the WECS in unstable operating regions. In the works of Banakar et al. and Luoet al.,18,19 the steady-state stability of a DFIG is analysed, and stable operating regions are proposed. However, therotor speed of the VSWT is controlled by the power converter, but a stable operation at the system fault condition isnot proposed. In the work of Chang-Chien,20 a control method to regulate the output power of the DFIG under variouswind conditions is proposed. The output power smoothing of the WECS is achieved by using the pitch angle controland the rotor inertia. However, the blade stress of the VSWT is increased drastically. Output power smoothing methodsof the pitch angle control are proposed in the works of Senjyu et al. and Rashad et al.21,22 However, these methodsreduce the WECS output power and increase the wind turbine blade pitching. In the aforementioned methods, thereare no discussions of the power smoothing efficiency. Therefore, an efficacy power smoothing method is required tothe WECS.

This paper presents a fuzzy controller-based output power smoothing method of the WECS by controlling the kineticenergy in inertia of a wind turbine. Further, a fuzzy controller-based grid side inverter control system is applied to inject asmooth power to the power grid. The WECS adopts an ACDCAC converter system, which is composed of a generatorside converter and a grid side inverter. The generator side converter controls the torque of the PMSG, whereas the gridside inverter controls the DC link voltage and the grid voltage, respectively. To achieve an efficient power smoothing ofthe WECS by using the inertia, one should consider two thingswind speed and the change of wind speed. Therefore, theproposed method includes the fuzzy logic23,24 to determine the speed command for the generator side converter. Inputsof the fuzzy controller are wind velocity and the change of wind velocity. From these inputs, the fuzzy controller decidesan adaptive averaging time to retrieve the kinetic energy from the inertia perfectly. As a result, the WECS can generate aconstancy smooth output power. The grid side inverter is controlled by the fuzzy controller-based PI controller to deliver asmooth power to the power grid. To perform a stable operation of the WECS at the fault condition, a shunt circuit includesin the DC link circuit. By using the proposed method, mechanical stresses of the wind turbine shaft, blades and genera-tor can be mitigated as compared with the conventional maximum power point tracking (MPPT) control method. It alsoensures an efficient power smoothing and transports a smooth power to the power grid. The proposed method is verified bynumerical simulations using MATLAB/Simulink (MathWorks, Inc., Natick, Massachusetts, United States).

2. WIND ENERGY CONVERSION SYSTEM

2.1. System configuration

The WECS integrates with a permanent magnet synchronous generator, a generator side converter and a grid side inverter.The configuration diagram of the WECS is shown in Figure 1. Wind energy is obtained from the wind turbine and is sent tothe PMSG. To generate the maximum output power, the rotational speed of the PMSG is controlled by a pulse width mod-ulation (PWM) converter. The output power of the PMSG is supplied to the power grid through a generator side converterand a grid side inverter.

PMSG~

Generator side converter

~

Grid side inverter Power system

PMSG control system

Figure 1. Wind energy conversion system configuration.

Wind Energ. (2013) 2013 John Wiley & Sons, Ltd.DOI: 10.1002/we

A. M. Howlader et al. Fuzzy control strategy for power smoothing and grid control

2.2. Wind turbine system

The wind turbine output power, Pw and the wind turbine output torque, Tw are defined by the following equations:

Pw D 12Cp.; /R

2oV

3w (1)

Tw D 12Cp.; /R

3oV

2w = (2)

where Vw is the wind speed, is the air density, Ro is the wind turbine blade radius, !w is the angular speed of the windturbine, Cp is the power coefficient, is the tip speed ratio, can be defined as D Ro!wVw , !w is the angular speed of thewind turbine and is the pitch angle. The power coefficient Cp is defined by the following equation.25

Cp D 0:22116

0:4 5

exp 12:5 (3)

D 11

C0:08 0:0353C1(4)

The wind turbine output power characteristics are depicted in Figure 2, from which it can be seen that, for any par-ticular wind speed, there is a rotational speed !opt, called the optimum rotational speed, which generates the maximumpower Pmax. In this way, the MPPT control for each wind speed increases the energy generation in the variable speed windgeneration system. !opt is calculated by differentiating Cp with respect to !w. Therefore, !opt is approximated by26

!opt D 0:2Vw 0:2: (5)

If !w D !opt, the wind turbine maximum output power Pmax can be obtained. The MPPT control is applied when the windspeed is less than the rated wind speed Vw_rated D 12 m/s to generate the maximum power. Above the rated wind speed, thepitch angle control system is applied to maintain the rated power of the PMSG. The pitch angle is operated in the followingcases. When the wind speed is within 0 < Vw < 5 m/s, there is no power generation, and the pitch angle is fixed at D 90.When the wind speed is within 5 < Vw < 12 m/s, the pitch angle operates at D 2 so that the power can be maximized.When the wind speed is within 12 < Vw < 24 m/s, the pitch angle operates at 2 90 so that the PMSG can maintainthe rated power 2 MW. Above the wind speed Vw > 24 m/s, the pitch angle is again fixed at D 90 for safety reasons.The different operating regions of the pitch angle control laws are shown in Figure 3(a). Figure 3(b) shows the pitch anglecontrol system that determines the pitch angle , where output power error Pg is used as the input of the PI controller.Actually, the pitch angle control system includes a hydraulic servo system. The system has nonlinear characteristics, butcan be modeled as a first-order lag system.27,28 Therefore, in this paper, the first-order lag system is used where the timeconstant is 1 s. Moreover, the pitch angle is limited by a limiter within 2 90, and the maximum rate of change is10/s.2931

2.5

0 1 2 3 40

0.5

1.0

1.5

2.0

Win

d tu

rbun

e ou

tput

pow

er P

g [M

W]

Wind turbine speed w [rad/s]

11m/s

12m/s

10m/s

9m/s

8m/s7m/s

6m/s

Pmax

Figure 2. Wind turbine output power characteristics.



Wind speed Vw

[m/s] 0

0.2

0.4

0.6

0.8

1.0

1.2

0 5 10 15 20 25 30

cut-in

rated cut-out

=90 deg =2 deg =2~90 deg =90 deg

(a)

+

-

PIs+11

90 deg

2 deg

90 deg

2 deg Rate limiterPref

Pg

Vw

P g 1s+1

CMD

+10 deg

-10 deg

Pitch angle control systemHydraulic servo systemAngle selector

(b)

Win

d tu

rbun

e ou

tput

po

wer

Pw

[p.u]

Figure 3. (a) Pitch angle control law for all operating regions and (b) pitch angle control system.

1/JeqD -

-

+Te

Tw

e

epg

s1

s1

Figure 4. Model of drive train.

dq

abc

e

Eq. (6)

Eq. (7)

Eq. (8)

vd

vq iq ic

ib

iaid

Te

dq

abc

e

va

vb

vc

Figure 5. Model of PMSG.

2.3. PMSG model

The mathematical model of a PMSG is the same as a permanent magnet synchronous motor. The voltage andtorque equations of the permanent magnet synchronous motor in the synchronous reference frame are given by thefollowing equations:

vd D .Ra C PLd/id !eLqiq (6)

vq D !eLdid C .Ra C PLq/iq C !eK (7)

Te D pfKiq C .Ld Lq/idiqg (8)where vd and vq are the dq-axis voltages, id and iq are the dq-axis currents, Ra is the stator resistance, Ld and Lq are thedq-axis inductances, !e is the electrical rotational speed, K is the permanent magnetic flux, P is the differential operatorand p is the number of pole pairs. In addition, the motion equation of the PMSG is given as

Te D Jeq d!gdt C D!g C Tw (9)

where D is the rotational damping, Jeq.Jeq D Jg CJw/ is the equivalent inertia, and !g is the mechanical rotational speed.The models of the drive train and the PMSG are given in Figures 4 and 5, respectively.



PMSG~

Converter

~

InverterPower system

e

abc dq

i1abc

Convertercontroller

v1dq

i1dq

abc dq

PWM

v1abc

sPLL

abc dq

Invertercontroller

i2dq

abc dqv2dq

PWM

v2abc

Shunt

i2abc

Figure 6. Power converter control system.

+_

i1d*

vi1d

PI+

_

*1d

+_

i1q*

vi1q PI ++

*1q

PI +*

_

Figure 7. Generator side converter control system.

2.4. Configuration of power converter system

The PMSG is connected to the grid through two six switches-based hard switched converters, with a DC link capacitor.The power converter control system is shown in Figure 6. The generator side converter is controlled through a PI controllersuch that the d -axis current is zero to obtain the maximum torque. The generator side converter achieves the variable speedoperation by controlling the rotational speed of the PMSG. On the other hand, the grid side inverter supplies the electricalpower, which is synchronized with the power system frequency. The DC link includes a shunt circuit to overcome the DClink overvoltages at the system fault condition. Therefore, a stable operation of the WECS can be achieved under a systemfault condition. Each configuration of the power converter control systems is described below.

2.4.1. Generator side converter.The generator side converter controls the rotational speed of the PMSG to perform the variable speed operation for the

MPPT controller. The vector control scheme is used and is shown in Figure 7. The speed control of the PMSG is real-ized on a rotating frame, where the rotational speed error is used as the input of the speed controller, which produces theq-axis stator current command i1q. Generally, a cylindrical pole type synchronous machine is considered to control thed -axis stator current, and i1d is set to zero. Therefore, the d -axis stator current command, i1d, is set to zero. The errorsbetween the dq-axis current commands and the actual dq-axis currents are used as the inputs of the current controllers.The current controller outputs produce the dq-axis voltage commands v1d and v1q after decoupling. The rotor position, e,is used for the transformation from abc to dq variables and is detected from the rotational speed of the PMSG by using amechanical sensor.

2.4.2. Grid side inverter.The grid side inverter controls the constant DC bus voltage Vdc and performs the unity power factor operation. The

control system of the grid side inverter is shown in Figure 8. The d -axis current can control the DC bus voltage Vdc, and theq-axis current can control the reactive power Qi . The DC bus voltage reference V dc is set to 3.5 kV, whereas the reactivepower command Qi is set to zero for the unity power factor operation. The phase angle s, for the transformation betweenabc and dq frame, is detected from the three-phase low voltage side of the grid-side transformer by using the phase-lockedloop system.32,33 The angular position of the dq reference frame is controlled by a feedback loop, which regulates theq-axis component to be zero, where the d -axis component depicts the voltage vector amplitude, and phase is determinedby the output of the feedback loop.



+_

i2d*

vi2d PI*2d

+_

i2q*

vi2q PI*2q

PI+

*_

+*

_

PI

Figure 8. Grid side inverter control system.

3. PROPOSED POWER SMOOTHING CONTROL SYSTEM

The power fluctuations should be minimized to prevent any kind of interruptions. There are many power generators that areequipped with the control system, which is called the automatic generation control (AGC).17 The AGC regulates the outputpower of the generator so that the power fluctuations attenuate. Since the AGC activates some mechanical and electricalactuators, which have a large time constant. It is effective for the low frequency power fluctuations. This section presentsthe proposed fuzzy-based output power smoothing method and the grid side inverter control techniques. Above the ratedwind speed, the output power is smoothed by the pitch angle control system, and below the rated wind speed, the outputis smoothed by the kinetic energy in inertia. Finally, at the fault condition, the DC link over voltage control method isalso described.

3.1. Fuzzy controller-based power smoothing scheme

This subsection presents the proposed fuzzy-based output power smoothing method by utilizing the kinetic energy in iner-tia of a wind turbine. The concept behind the proposed power smoothing method depends on two events:

Increase in wind turbine kinetic energy due to the acceleration of the turbine rotational speed at the high wind speed. A discharge of wind turbine kinetic energy due to the deceleration of the turbine rotational speed at the low

wind speed.

These two incidents can generate a smooth output power of the WECS. The maximum output power of the wind turbine,Pmax is calculated as

Pmax D Tw!opt (10)The average value of the maximum wind turbine output power is calculated as

P D 1T

Z ttT

Pmaxdt (11)

where t denotes the present time and T is the averaging time. Then, the power difference P between the maximum outputpower and the average value of the maximum output power is determined. The P gives the storing and restoring power ininertia of the wind turbine. Therefore, the

RP gives the wind turbine storing and restoring energy. Wind turbine kinetic

energy, E, is determined as

E D 12Jeq!

2g (12)

The wind turbine rotational speed command, !g , is determined from the kinetic energy command, E (the sum ofRP

and E), as

!g Ds

2EJeq

(13)

Finally, the generator electrical reference speed !e for the proposed method is determined from the number of pole pairsp as

!e D !g p (14)



2 4 6 8 10 1230

35

40

45

50

55

Sum

mat

ion

of g

ener

ated

po

wer

(MW

)

Averaging time T (s)

Maximum output power

(a) Averaging time Vs. total output power characteristics.

0 10 20 30 400

20

40

60

Time (s)

Ener

gy fu

nctio

n *P m

ax

(MJ) Constant averaging time

MPPT method

T =5 s

T =2 s

T =8 s

(b) Maximum output power function.

0 10 20 30 400

20

40

60

Time (s)

Pow

er sm

ooth

ing

func

tion

*P l

evel

(M

W)

Constant averaging timeMPPT method

T =5 s

T =2 s

T =8 s

(c) Power smoothing function.

Figure 9. Characteristics of averaging time. (a) averaging time versus total output power characteristics, (b) maximum output powerfunction and (c) power smoothing function.

and for the MPPT control method,

!e D !optp: (15)

The averaging time, T , in equation (11) is an important parameter for the output power smoothing control system.Figure 9(a) shows the characteristic between the averaging time and the summation of output power of the WECS. Thedotted line shows the summation of output power for the MPPT controller during a simulation period. The output powermoves to the maximum power at the short averaging time. From this figure, (Figure 9(a)), it can be realized that a constantaveraging time may decrease the system efficiency. At the short averaging time, the total generated power is similar to theMPPT control method, but the power fluctuation is also the same as the MPPT control method. On the other hand, thelarge averaging time can generate a smooth power, but total generated power of the WECS is decreased. There are othercharacteristics of the averaging time and the power smoothing for the detailed analyses. Evaluation of the output power(Pg) smoothing is represented by the maximum energy function Pmax and the smoothing function Plevel, which areexpressed as21



Pmax DZ t

0Pg.t/dt (16)

Plevel DZ t

0

dPg.t/dt

dt : (17)

If Pmax is large, the efficiency of the WECS increases. If Plevel is small, the output power fluctuation decreases.Figure 9(b) and (c) shows the maximum energy function and the output power smoothing function for various averag-ing times. There is a comparison between the MPPT control method and the power smoothing method at the constantaveraging time. From these two figures, efficiency of the WECS is increased (Figure 9(b)), and smoothing performance ofthe WECS is decreased (Figure 9(c)) during a short averaging time. At the high averaging time, efficiency of the WECSis decreased, and the smoothing performance of the WECS is increased. When the wind velocity fluctuates rapidly, thelarge averaging time is suitable for the WECS to generate a smooth output power. Alternatively, when the wind velocitybecomes steady, the short averaging time can increase the efficiency of the WECS. So, an adaptive averaging time is nec-essary to improve the overall performance of the output power smoothing method (instead of a constant averaging time). Itcan be implemented through the fuzzy controller. Wind velocity and the fluctuation of wind velocity are determined withan appropriate averaging time instantaneously.

The fuzzy logic is described by a set of if then-based fuzzy rules.23,34 It is effective when the mathematical expres-sions are difficult due to the inherent complexity, nonlinearity or non-clarity. There are two inputs of fuzzy reasoning suchas wind velocity .Vw/ and the fluctuation of wind velocity d=dt .Vw/. The fuzzy input membership functions (i.e., windvelocity and the change of wind velocity) are shown in Figure 10(a) and (b), respectively. Output membership functionsand fuzzy rules are shown in Figure 10(c) and (d), respectively. The membership functions can be tuned according to thespecifications of generators and turbines. The i th fuzzy rule is expressed as35

Rule i : if Vw is wvx and d=dt .Vw/ is cwvy then T is ATlx D 1; 2; : : : ; 8; y D 1; 2; : : : ; 8; l D 1; 2; : : : ; 8

where wvx, cwvy denote the antecedents, and ATl represents the consequent part. The fuzzy reasoning output (i.e.,averaging time) T is calculated by

T DP64

iD1 !iATlP64iD1 !i

(18)

where !i denotes the grade for the antecedent and is obtained by

!i D .! wvi /.! cwvi / (19)where ! wvi and ! cwvi are grades of the antecedents for each rule. The proposed fuzzy controller-based powersmoothing control system is shown in Figure 11. The final outcome of this controller is the electrical reference speed !e ,and is the input of the converter controller. It can control the speed and the generator output power, and delivers an efficientoutput power.

The rotational speed of wind turbine !r is proportional to the wind speed. Therefore, the wind turbine rotational speedand the change of wind turbine rotational speed can be inputs of the fuzzy controller rather than the wind speed and thechange of wind speed. If the measurement of the wind speed is difficult in a place, the rotational speed and the change ofrotational speed of wind turbine will utilize as the inputs of fuzzy controller for the proposed system.

3.2. Fuzzy controller-based grid side inverter control method

Due to the non-linear behavior of the power system and the linearization problems, the control of the variable speed WECSis difficult by using the conventional PI controller methods. For example, the PI controller design requires to identify thewind turbine transfer function, the linear model of the network and defining an accurate tuning process. Because of thesereasons, to implement a PI controller is not an easy task. The fuzzy controller is a rule-based non-linear control technique.The fuzzy controller can overcome these problems and presents some advantages as compared with a PI controller. It iseasy to obtain the variable gains depending on the errors and overcome the problems, which are affected by an uncertainmodel.3639 In this study, a fuzzy controller-based inverter control system is shown in Figure 12(a). In this figure, theconventional PI controller is replaced by the fuzzy controller. Figure 12(b) shows the detail of the fuzzy-based PI con-trol system. It is a multi-input multi-output-based system.40 The proposed controller is designed to control the smoothDC link voltage and the unity power factor operation. Consequently, it can deliver the smooth power to the power grid.Figure 13 shows fuzzy input membership functions (i.e, error and change of error) and the output membership functions(i.e., proportional gain Kp and integral gain Ki ), and fuzzy rules.



wv1 wv2 wv3 wv4 wv5

4 100

0.5

1

Wind Velocity

wv6 wv7 wv8

16

(a) Membership function of wind velocity.

cwv1 cwv2 cwv3 cwv4 cwv5

00

0.5

1

d/dt (Wind Velocity)

cwv6 cwv7 cwv8

2.2

(b). Membership function of change of wind velocity.

AT1 AT2 AT3 AT4 AT5

6 00

0.5

1

Averaging Time T (s)

AT6 AT7 AT8

8

(c) Membership function of averaging time.

cwv1d/dt (V_ )

V_

cwv2 cwv3 cwv4 cwv5 cwv6 cwv7 cwv8wv1wv2wv3wv4wv5wv6wv7wv8

AT1AT1AT1AT1AT1AT1AT1AT2

AT1AT1AT1AT1AT1AT1AT2AT3 AT4

AT2AT1AT1AT1AT1AT1

AT5AT4AT2AT2AT1AT1AT1AT1 AT1

AT2AT2AT2AT2AT3AT5AT6




AT3

(d) Table I (fuzzy rules).

Figure 10. Fuzzy controller for power smoothing enhancement. (a) membership function of wind velocity, (b) membership functionof change of wind velocity, (c) membership function of averaging time and (d) Table I (fuzzy rules).)

T P

P*P

E

E*g+

+

+

-

Eq. (10)Eq. (11)

Eq. (13)Eq. (2)

Eq. (5)V

opt

max

Eq. (12)Pitch AngleController

Td/dt Fuzzy controller

Eq. (14)e

Figure 11. Power smoothing control system for WECS.



+

_

i2d*

vi2d PI*

2d

+

_

i2q*

vi2q PI*

2q

+*

Qi

_

+

*

_

V dc

V dc

Qi

FuzzyController

FuzzyController

e

e

(a) Fuzzy controller based inverter control system

e

ddt

Fuzzy LogicController

Kp

Ki

x

x

++

ref i 2d*

2q, i* }{

ce

(b) Fuzzy controller based PI control system.

Figure 12. Proposed inverter control system. (a) fuzzy controller-based inverter control system and (b) fuzzy controller-based PI controlsystem.

The fuzzy rules and membership functions presented in fuzzy controller (i.e., power smoothing control and invertercontrol) are determined by the trial-and-error process. However, it is possible to tune the parameters of the controllers andmembership functions of the fuzzy controller to achieve the smoothing and maximum acquisition of the available windpower. Various methods have been proposed for tuning the fuzzy controller, such as self-tuning algorithm based on anexperimental planning method,41 where the scaling factors of optimal parameters can be determined efficiently accordingto the desired performance indexes, Taguchi tuning method,42 and tuning the membership functions.43 However, in thepresent literature, the selection of scaling factors is still based on the trial-and-error method.

3.3. Configuration of the DC link circuit

The DC link voltage, Vdc is controlled by the grid side inverter. Therefore, under the system fault condition, it is verydifficult to control the over DC link voltage through the conventional PI controller. Hence, a shunt circuit includes in theDC link capacitor as shown in Figure 14(a). The DC link voltage control system is shown in Figure 14(b). From this figure,when the grid voltage, Vt, is less than 0.8 pu (i.e., fault occurred) then the error between the DC link voltage command V dc,and the DC link voltage Vdc, is the input of the PI controller, and the switching command of the shunt circuit is determinedby the triangular PWM law.

4. SIMULATION RESULTS

The power system model is shown in Figure 15. The grid side inverter is connected to an infinite bus through an LCfilter, a three-phase transformer and a transmission line. The parameters of the PMSG and the wind turbine are listed inAppendix. To evaluate effectiveness of the proposed method, the WECS operations are assessed in the steady-state and thesystem fault conditions. The evaluation of the steady-state condition is verified through the fuzzy controller-based powersmoothing method and the grid side inverter control system.

4.1. Operation in fuzzy controller-based power smoothing system

Simulation results of power smoothing are shown in Figure 16. Simulation results among the MPPT control method, thefuzzy controller-based method (i.e., adaptive averaging time) and the without fuzzy controller (i.e., constant averaging timeT =6 s) method are compared. In these figures, the fuzzy controller-based method is referred as the proposed method, and



PMPS PBZONSNMNB

-800

-60 -40 -20 0 20 40 60 80

0.5

1

Error, e(a) Membership function of error.

PMPS PBZONSNMNB

00 20

0.5

1

-20Change of error, ce

(b) Membership function of change of error.

PMPS PBZONSNMNB

0

0.5

1

1 1.5 2Proportional gain, Kp

(c) Membership function of Kp.

PMPS PBZONSNMNB

0

1

0.5

30 701Integral gain, Ki

(d) Membership function of Ki.

K p i NB NM NS ZO PS PM PBNBNMNSZOPSPMPB

NBNBNM

NB NM

NM

ZO

NSZOPS

NS

ZOPSPM

NSZOPSPMPB

NMNSZO

PS

PMPB

PB

NSZOPS

PMPB

ZO

PM

e

ce

(e) Table II(fuzzy rules).

K&

Figure 13. Fuzzy PI control system. (a) membership function of error, (b) membership function of change of error, (c) membershipfunction of Kp and (d) membership function of Ki.



RdcSW

C

idc

Power systemGenerator

(a) Configuration of DC-link circuit.

+

_

Vdc

Vdc

*

PI1

00

SW+1

1

Grid voltage v

0.8 put

v t 0.8 1v t 0.8 0

_

Carrier wave

(b) DC-link voltage control technique.

Figure 14. DC link voltage control for WECS. (a) configuration of DC link circuit and (b) DC-link voltage control technique.

PMSG AC-DC-AC LCfilter

1.5kV/6.6kV

Grid P , Qtt

Fault

AC load

vt

5 MW

Inf. BUS

Figure 15. Power system configuration.

the without fuzzy controller based is referred as the conventional method. Wind velocity is shown in Figure 16(a). Windspeed data contain significant fluctuation and high turbulence after 25 s. The change of wind speed is derived by the firstderivative of wind speed. It is shown in Figure 16(b). Figure 16 (a) and (b) shows the inputs of the fuzzy controller. Theoutput of the fuzzy controller is the averaging time, which is depicted in Figure 16(c). The characteristics of the kineticenergy of the wind turbine is shown in Figure 16(d). The kinetic energy comes up when wind speed increases, and comesdown the kinetic energy when wind speed decreases. The generator electric torque is shown in Figure 16(e). The fuzzycontroller-based method can reduce the torque fluctuation significantly. As such, it can reduce the mechanical stress of agenerator. A comparison of the pitch angle is shown in Figure 16(f). From this figure, the proposed method can lessen theblade pitching as compared with the MPPT control system. Therefore, the proposed fuzzy controller-based method candiminish the stress of the wind turbine blades. Figure 16(g) shows the generated output power of the WECS. The proposedmethod can reduce the power fluctuation extensively as compared with the MPPT control method. The proposed methodcan generate efficient output power smoothing at different phases of wind velocity as compared with the without fuzzycontroller method. The without fuzzy controller method can generate a smooth output power but it reduces the outputpower extensively as compared with the MPPT control method. As a result, the efficiency of the output power is reduced.The proposed method generates a tiny fluctuation as compared with the without fuzzy controller method, but it can ableto generate an efficient output power of the WECS. The torque difference of the WECS is shown in Figure 16(h). Theproposed method can reduce the torque difference significantly as compared with the MPPT control method. As a result, itcan mitigate the shaft stress of the WECS.



0 10 20 30 40

0 10 20 30 40

8

10

12

14

Time [s]

Win

d sp

eed

V w[m

/s]

(a)

Time [s](b)

0

0.5

1

1.5

2

Chan

ge o

f win

d sp

eed

d/dt

( Vw

)

0 10 20 30 406

6.5

7

7.5

8

Time [s]

Ave

ragi

ng ti

me

T

[s]

(c)

0 5 10 15 20 25 30 35 4002468

1012 x 104

Time [s]

Kin

etic

ene

rgy

E[J]

(d)

108642

02 x 105

PMSG

ele

ctric

torq

ue T

e[Nm]

Proposed methodConventional methodMPPT method

0 10 20 30 400

5

10

15

20

25

Time [s]Pi

tch

angl

e

[deg

](f)

0 10 20 30 40Time [s]

(g)

0

0.5

1

1.5

2

2.5

Gen

erat

ed o

utpu

t pow

er P

g [M

W]

5 10 15 20 25 30 35 40

0.20.1

00.10.20.3

Time [s]Torq

ue d

iffer

ence

Tdi

ff [M

Nm]

(h)





0 5 10 15 20 25 30 35 40Time [s]

(e)

Figure 16. Simulation results (a) wind velocity Vw, (b) change of wind velocity, (c) averaging time T , (d) wind turbine kinetic energy E,(e) generator electric torque Te, (f) pitch angle , (g) output power of WECS Pg and (h) torque difference Tdiff.

Figure 17 shows simulation results of the proposed control system at a high wind velocity. Wind velocity is shown inFigure 17(a). From this figure, wind velocity exceeds the cut out speed. Outcomes of the proposed controller are shownin Figure 17(b)(d). Figure 17(b) shows the kinetic energy of the wind turbine. Due to the cut out wind speed, the pitchangle increases rapidly to protect the wind turbine (as shown in Figure 17(c)). The output power of the PMSG is shownin Figure 17(d). From this figure, the output power of the PMSG decreases swiftly due to the pitch actions of the cut outspeed. When wind velocity reduces from the cut out speed, the pitch angle also decreases, and the PMSG delivers theoutput power again.



0 5 10 15 200

10

20

30

Time [s]

Win

d sp

eed

V w

[m

/s]Cutout wind speed

(a)

0 5 10 15 200

0.5

1

1.5

2 x 106

Time [s]

Kin

etic

ene

rgy

E

[J]

(b)

0 5 10 15 200

50

100

Time [s]

Pitc

h an

gle

[

deg]

(c)

0 5 10 15 201

0

1

2

3

Time [s]

Out

put p

ower

Pg

[MW

]

(d)

Figure 17. Simulation results (a) wind velocity Vw, (b) kinetic energy E, (c) pitch angle and (d) output power of WECS Pg.

5 6 7 8 9 10 11 12 13 14 1510

11

12

13

14

Time [s]

Win

d sp

eed

V w [m

/s]

(a)

5 10 153560

3580

3500

3520

3540

Time [s]

DC-

link

volta

ge V

dc[V

]

Conventional methodProposed method

6 7 8 9 11 12 13 14

(b)

5 6 7 8 9 10 11 12 13 14 151.2

1.4

1.6

1.8

2

2.2

Time [s]Act

ive

outp

ut o

f grid

sid

e in

verte

r P t

[M

W]


(c)

5 6 7 8 9 10 11 12 13 14 15

0.016

0.018

0.02

0.022

0.024

Time [s]Rea

ctiv

e ou

tput

of g

rids

ide

inve

rter

Q t [M

var] Conventional method

Proposed method

(d)

Figure 18. Simulation results (a) wind velocity Vw, (b) DC link voltage Vdc, (c) active power of inverter Pt and (d) reactive power ofinverter Qt.

4.2. Operation in fuzzy controller-based grid side inverter control system

Simulation results of the fuzzy-based inverter control system are shown in Figure 18. Simulation results are shown asa comparison between the conventional PI controller method and the fuzzy-based PI controller method. Wind speed isshown in Figure 18(a). As shown in Figure 18(b), the proposed fuzzy-based PI controller can reduce the DC link voltagefluctuation as compared with the conventional PI controller. So, it ensures the smooth operation of the DC link capacitorand increases the lifetime of capacitor. The active and reactive powers are delivered to the power grid through the inverter,which are shown in Figure 18(c) and (d). The proposed method can deliver smooth power to the power grid as comparedwith the conventional method.

4.3. Operation in fault condition

Simulation results of the fault condition are shown in Figure 19. The three-line-to-ground fault occurs at the middle ofthe transmission line in Figure 15, and electric power supply to the load is stopped. The sequences of the simulation aredescribed as follows:



2 3 4 5 6 7 810

11

12

13

Time [s]

Win

d sp

eed

V w

[m

/s]

(a)

2 3 4 5 6 7 80

2

4

6

8

Time [s]

Pitc

h an

gle

[de

g]

(b)

1 2 3 4 5 6 7 80

0.5

1

1.5

2

2.5

Time [s]

Gen

erat

ed o

utpu

tpo

wer

Pg

[MW

]

(c)

2 3 4 5 6 7 8Time [s]

3.504.00

DC-

link

volta

ge V

dc[k

V]

4.50

5.50

3.00

2.50

2.00


(d)

2 3 4 5 6 7 8-2

0

2

4

6

Time [s]

Act

ive

outp

ut p

ower

of

grid

-sid

e in

verte

r P t

[M

W]

Without shuntWith shunt

(e)

2 3 4 5 6 7 80

0.5

1

1.5

2

2.5

Time [s]

Rea

ctiv

e ou

tput

pow

er o

fgr

id-s

ide

inve

rter

Q t [M

Var]

Without shuntWith shunt

(f)

Figure 19. Simulation results (a) wind velocity Vw, (b) pitch angle , (c) WTG system generated output power Pg, (d) DC link voltageVdc, (e) active power of grid side inverter Pt and (f) reactive power of grid inverter Qt.

At simulation time 6.0 s, the three-line-to-ground fault occurs at the middle of the transmission line. When the AC grid voltage is within vt


a shunt circuit uses in the DC link circuit. Therefore, a stable operation of the WECS can achieve during the system faultcondition. From simulation results, effectiveness of the proposed methods are verified.

APPENDIX

Simulation parameters of the WECS are as follows:

Wind turbine: blade radius Ro D 41 m, inertia Jeq D 8000 kgm2, air density D 1:205 kg/m3, rated wind speedVw_rated D 12 m/s, cut-in speed Vw;cutin D 5 m/s and cut out speed Vw;cutout D 25 m/s.

Parameters of generator and grid network: rated power Pg_rated D 2 MW, number of poles pair p D 80, statorresistance Ra D 0:1 , inductance L D 0:005 H, field flux K D 10:68 Vs/rad, rotational damping D D 0, gridfilter impedance, D 0:05 C j 0:3 pu.

Parameters of power converter: PWM carrier frequency fp D 10 kHz, rated DC link voltage Vdc_rated D 3:5 kV,DC link capacitor C D 15 mF, shunt circuit resistance Rs D 2:5 .

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