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Abstract -- Wind energy has been receiving more acceptance as

a reproducible, resourceful and clean energy source since last

decade. Wind power is not constant and can fluctuate below the

rated wind power when the wind speeds are lower than the rated

wind speed. This fact affects the grid the wind generators are

connected to, which is becoming more significant with the

increasing penetration of wind energy systems. Pitch angle

control has been one of the most common methods for smoothing

output power fluctuations during below rated wind incidents. Inthis paper, two methods of fuzzy logic system have been proposed

for the pitch angle controller: the determination of the command

output power based on Exponential Moving Average (EMA) with

a proper selection of correction factor by fuzzy reasoning and the

dynamic selections of target output power according to the

current wind incident. Simulation results show the effectiveness

of the proposed methods in smoothing wind power fluctuations

with significantly small drop of output power.

Index Terms--Control design, Fuzzy logic, Power smoothing,

Wind energy.

I. I NTRODUCTION

N recent years, wind energy has drawn a great concern dueto its inherent attribute of reproducible, resourceful and

pollution-free characteristics against high depletion of

conventional energy sources. Moreover, wind energy source

has been competing with conventional generation sources as a

result of reduction in wind energy costs with technological

advancements, incentives and financing options for

developing renewable energy facilities since last decade. All

these factors have caused wind power to become the fastest

growing energy source.

In this situation, wind farms begin to influence power

systems more extensively, which brings new challenges to the

power system with the high penetration of wind power. One of

the challenges is that wind power is not constant and can

fluctuate below the rated power, leading to the fluctuation of

M. A. Chowdhury is with Faculty of Engineering and Industrial Sciences,

Swinburne University of Technology, VIC 3122, Australia (e-mail:[email protected]).

N. Hosseinzadeh is with Faculty of Engineering and Industrial Sciences,

Swinburne University of Technology, VIC 3122, Australia (e-mail:

[email protected]).

W. Shen is with Faculty of Engineering and Industrial Sciences, Swinburne

University of Technology, VIC 3122, Australia (e-mail: [email protected]).

frequency and the occurrence of voltage flicker at the buses of

the power system the wind generators are connected to. This

fact can cause instability problem in the power system,

especially when there are loads so sensitive to accept the

variations of voltage and frequency [1].

The importance of smoothing output power fluctuations

becomes more significant with the increasing penetration of

wind energy systems in the grid. A wind farm with many windturbines has the natural tendency of smoothing output power

fluctuation. But, if synchronization of output power

fluctuation from synchronization phenomena is generated, the

effect of smoothing power may be lost [2]. Recently, the

provision of power storage system has been proposed in [1],

[3]-[5]. This is a very effective strategy when power quality is

concerned for high sensitive loads, but it is not efficient from

the economic point of view.

On the other hand, pitch angle control has now become a

very popular method for smoothing wind power fluctuations.

A fuzzy logic controller was used to ensure a reliable

smoothing operation at low cost [1]. Reference [6] used a

minimum variance controller for smoothing purpose. This

method compensated the influence of parameter variations.

Robust stability was achieved using H∞ controller [7]. A

generalized predictive controller with a fuzzy reasoning

corrector was used to provide stability operations during the

rapid change in operating points [8]. In spite of having specific

advantages of applying specific pitch angle control strategy,

some drawbacks are yet to overcome. The control methods

stated above partially smooth the output power fluctuations

and hence problems originated by the output power

fluctuations are partially solved, but they resulted in a large

drop in output power.

In this paper, two methods of Fuzzy Logic System (FLS)

have been proposed for the pitch angle controller, which is

based on the motivation of trading off between a complete

smoothing of wind power fluctuations and drops in output

power in an optimum way during below rated wind incidents.

The first method is based on the determination of the

command output power through Exponential Moving Average

(EMA) with a proper selection of correction factor by fuzzy

reasoning to make output power to follow that command value

Fuzzy logic systems for pitch angle controller

for smoothing wind power fluctuations during

below rated wind incidentsM. A. Chowdhury N. Hosseinzadeh, Member, IEEE W. Shen, Member, IEEE

I

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by dynamic pitch actuation. The second method is based on

the dynamic selections of target output power values

according to the wind incident and limit power to obtain the

target output by dynamic pitch actuation. The effectiveness of

the proposed methods has been demonstrated in smoothing

wind power fluctuations with significantly small drop of

output power.

II. WIND POWER GENERATION SYSTEM

The aerodynamics of the wind turbine is characterized by

Cp- λ- β curve, which is usually provided by the manufacturers.

Cp is the power coefficient, it corresponds to maximum

mechanical power extraction from wind at its maximum value

and is a function of the tip-speed ratio ( λ) and the pitch angle

( β ). For a given Cp, the mechanical power ( P m) and

mechanical torque (T m) extracted from the wind by the wind

turbine can be expressed as [9]

( )

2

,3

W pm

V AC P

β λ ρ = (1)

t

mm P T

ω = (2)

where, ρ is the air density, A is the sweep area of the blades,

V W is the wind speed and ωt is the turbine rotor speed.

Conventionally, the rotor is treated as two lumped masses,

i.e. turbine mass and generator mass are connected together by

shaft with a certain damping and stiffness coefficient values.

Neglecting the turbine and generator self-damping, shaft

stiffness and torsional oscillations, the mathematical equation

can be expressed as [9]

( ) em g

g t T T dt

d H H −=+

ω 2 (3)

where, H t is the turbine inertia constant, H g is the generator inertia constant, ω g is the generator rotor speed and T e is the

electromagnetic torque.

For the induction generator, a synchronously rotating d-q

reference frame of 4th order is used, which is rotating with the

same speed as the stator voltage. Stator and rotor voltages in

this reference frame are given in the following equations [9]

dt

d i Rv ds

bqsds sds

φ

ω φ

1+−−= (4)

dt

d i Rv

qs

bdsqs sqs

φ

ω φ

1++−= (5)

dt

d si Rv dr

bqr dr r dr

φ

ω φ

1+−= (6)

dt

d si Rv

qr

bdr qr r qr

φ

ω φ

1++= (7)

where, v is the voltage, i is the current, R is the resistance, s is

the slip, φ is the flux and ωb is the angular frequency. Suffix s,r , d and q denote stator, rotor, d -axis component and q-axiscomponent, respectively.

The electromagnetic torque, T e is expressed as

)dsqsqsdse ii pT φ φ −= (8)

where, p is the number of pole pairs.

The power output, P g of the wind turbines is calculated as

qr qr dr dr qsqsdsds g iviviviv P −−+−= (9)

Parameters of the wind turbine used in this work are shownin Table I [10].

TABLE I

WIND TURBINE PARAMETERS

Parameter Symbol Value Unit

Nominal mechanical output power P mec 1.5 MW

Nominal electrical power P elec 1.5/0.9 MW

Nominal voltage (L-L) V nom 575 Volt

Stator resistance R s 0.00706 p.u.

Stator inductance Lr 0.171 p.u.Rotor resistance Rr 0.0058 p.u.

Rotor inductance Lr 0.156 p.u.

Magnetizing inductance Lm 2.9 p.u.Base frequency f 60 Hz

Inertia constant H 1 p.u.

Friction factor F 0.01 p.u.

Pair of poles p 3 -

The control system generates the voltage command signalvr and v gc for the rotor and grid side converters respectively in

order to control the DC voltage and the reactive power or thevoltage at the grid terminals. The other part of the control

system is a pitch angle controller.

III. CONVENTIONAL PI PITCH A NGLE CONTROLLER

The conventional Proportional Integral (PI) controller isused to maintain the output power of the wind turbine at its

rated value by adjusting the pitch angle of the blades, which

provides an effective means of regulations in strong windspeeds. Fig. 1 shows a conventional simplified PI controller,

which regulates the output in accordance with the error (e)

between generator rotor speed (ω g ) and its upper limit

(reference) value.

Fig. 1 Control scheme of the conventional PI pitch angle controller.

This reference is chosen in such a way so that a minimum

possible reference value enables generation of maximum

possible power (1 p.u.). The minimum value is expected to bechosen as higher generator rotor speed is vulnerable to power

system stability. For the specific wind turbine system adoptedin [10], this reference value has been chosen as 1.21 p.u.

The error signal (e) is then sent to the PI controller which

produces the command pitch angle ( β c’ ). It reduces the turbineoperating efficiency to minimize power coefficient so that the

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generator maintains its control speed value. Pitch servos are

employed for proper positioning of the blades, which is

modelled by using a first order delay system. The pitchactuation system cannot respond instantly and is also limited

by its actuation speed. That is why servo delay and pitch rate

are included to get more realistic responses from the pitch

angle control system, which gives the value of pitch angle ( β ).

All parameters of this controller are given in Table II [1].

TABLE II

PARAMETERS OF THE PITCH A NGLE CONTROLLER

Parameter Symbol Value Unit

Servo system delay T d 0.25 s

Rate limiter d β /dt ±3 o/s

Proportional gain K P 100 -Integral gain K I 10 -.

IV. FUZZY LOGIC SYSTEMS FOR SMOOTHING WIND POWER

FLUCTUATIONS

Fuzzy logic systems apply reasoning, similar to how

human beings make decisions, and thus contain expertknowledge of the system. This knowledge is reflected in a

series of rules, which the controller uses to derive its output

signal from its input signals. The essential premise of

designing the FLS is not an accurate description of the system,

but expert knowledge about the likely behaviour of the

system. The advantages of using fuzzy logic in the windturbine system are that the wind turbine system needs not to be

accurately described nor does it need to be linear.

Two methods of the FLSs have been proposed for the pitch

angle controller for smoothing output power fluctuations by

generating pitch angle ( β c*). Fig. 2 shows the proposed design

of the pitch angle controller.

Fig. 2. Control scheme of the proposed pitch angle controller.

A. Method 1

For smoothing wind power fluctuations, the most

important part is to determine the command output power ( P g_com’ ). It has been determined by a popular smoothingmethod, known as the EMA, so that the generated output

power follows the command value for smoothing purpose.

This command value has been calculated using the EMA in

the following manner [11]

( ) P C P com g α α −+= 1' _ (10)

where, C is current value, P is previous period’s value and α is

the smoothing constant.

In this paper, 2 periods of average value (each of 1 s) is

used in simulation. So, EMA starts from 2 s when 2 period’s

data are available. Smoothing constant (α) is chosen as 0.8,

which indicates that 80% weightage has been considered for

the data of present period with 20% exponentially decreasing

weightage to the previous periods. The superiority of the

EMA over other traditional smoothing technique is that the

EMA can follow wind speed more rapidly than other

smoothing techniques because it uses its data of previous

periods for next calculation [1].

If the output power error for the difference of the reference

output power (the output power with no generation of pitch

angle during below rated wind incidents has been considered

as reference output power, P g_ref ) and output power command

is negative, the command value is larger. In that case, thecontroller gets no useful information for actuating pitch for

smoothing purpose. So, a concept of correction factor (k ) has

been introduced to allow the command output power ( P g_com)

to always retain smaller value than the reference value by

relating the EMA output power command ( P g_com’ ) in the

following manner

' _ _ com g com g P k P ×=

(11)

The correction factor has been assessed by applying fuzzy

reasoning. Variation of reference output power from the

command output power (e) and reference output power ( P g_ref )

are used as inputs. The system has triangular membership

functions with overlapping. Fig. 3 shows the input and output

membership functions. The linguistic variables are represented by N, ZE, P (Positive), S (Small), M (Medium), L (Large), XS

(Extra Small), XL (Extra Large) and XXL (Double Extra

Large).

The system has got 12 rules, which are listed in Table III.

The ith fuzzy rule is expressed by [8] asRule i: if e(n) is Aa and P g_ref (n) is Bb,

then k (n) is C c.

a= 1, 2, 3; b= 1, 2, 3, 4; c= 1, 2, …., 12 (12)

where, Aa and Bb are antecedents and C c are consequents.

TABLE III

R ULES FOR DETERMINING CORRECTION FACTOR

k e

N ZE P

P g_ref ZE XS S XXL

S XS S XL

M XS S L

L S S L

Correction factor (k ) has been crisped by the Centre of

Gravity (CoG) defuzzification method, which returns the

centre of the area under the curve representing the aggregatedoutput fuzzy set. It is calculated in the following manner

∑ ∑= =

=

12

1

12

1

/)(

i i

iciC nk ω ω (13)

Fig. 4 shows that we have achieved command output

power ( P g_com), which is lower than the reference output power

( P g_ref ) over the whole 300 s period from the EMA command

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output power ( P g_com’ ) by the generation of correction factor

(k ).

Fig. 3. Fuzzy sets and their corresponding membership functions for

determining a proper correction factor: (a) e, (b) P g_ref and (c) k .

Fig. 4. Obtaining command output power from EMA command output power by the generation of correction factor.

To obtain command pitch angle ( β c*) using Method 1,variation of reference output power from the command output

power (e) and its variation during a sampled time ( Δe) are

used as inputs. The system has triangular membership

functions with overlapping. Fig. 5 shows the input and output

membership functions. The linguistic variables are represented

by NXL (Negative Extra Large), NL (Negative Large), NM(Negative Medium), NS (Negative Small), ZE, PS (PositiveSmall), PM (Positive Medium), PL (Positive Large), PXL

(Positive Extra Large), ZEP (Zero Plus), XXS (Double Extra

Small), XS, S, M, L, XL and XXL.

Fig. 5. Fuzzy sets and their corresponding membership functions using

Method 1: (a) e, (b) Δe and (c) β c*.

It has got 45 rules, which are formed in similar way of (12)and listed in Table IV. Command pitch angle has been crisped

by the CoG defuzzification method using (13).

TABLE IV

R ULES OF FUZZY LOGIC SYSTEM (METHOD 1)

β c* Δe

NL NS ZE PS PL

e NXL XXS S ZE S L

NL XS M ZEP M XL

NM S L XXS L XXL

NS M XL S XL XXL

ZE L XXL S XXL XXL

PS XL XXL L XXL XXL

PM XXL XXL L XXL XXL

PL XXL XXL XXL XXL XXLPXL XXL XXL XXL XXL XXL

B. Method 2

For smoothing wind power fluctuations, the generated

wind power ( P g ) has been categorized (stepped by 0.05 p.u.)

into stages, which is defined as ‘Power Stage (PS)’. Minimum

value would be taken as a target value ( P g_tar ) for the

controller of each corresponding PS. If the generated wind

power falls under a PS, a pitch angle would be actuated to

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shed mechanical power and limit the wind power to P g_tar of

that corresponding PS. For example, if P g =0.78 p.u. at any

instant, a pitch angle would be actuated to make P g =0.75 p.u.

(see PS3 in Table V).

TABLE V

POWER STAGES

Power stage Range P _tar

PS1 0.9<Pg≤0.85 0.85PS2 0.85<Pg≤0.8 0.8PS3 0.8<Pg≤0.75 0.75

.

.

PS16 0.15<Pg≤0.1 0.1

PS17 0.1<Pg≤0.05 0.05

PS18 0.05<Pg≤0 0

To obtain command pitch angle ( β c*) using Method 2,

variation of reference output power from the target value (e)

and its variation during a sampled time ( Δe) are used as inputs.

Output power has been divided into stages instead of generator

rotor speeds which make the control design easier because theuse of the rotor speed would cause non-linear.

Fig. 6. Fuzzy sets and their corresponding membership functions using

Method 2: (a) e, (b) Δe and (c) β c*.

This system also has triangular membership functions with

overlapping. Fig. 6 shows the input and output membership

functions. The linguistic variables are represented by ZE, ZEP,

XXS, XS, S, M, L, XL, XXL, NS, NL, PS and PL.

The range of the command pitch angle output is different

for each corresponding PS. It generally requires more pitch

angle generation as wind power falls in PS of lower levels.The values of x1 and x2 of Fig. 6c are listed in Table VI. The

same values of x1 and x2 for PS1 and PS18 refer to singleton

output membership function.

TABLE VICOMMAND PITCH A NGLE R ANGE FOR FLC-B (METHOD 2)

Power stage x1 x2 Power stage x1 x2

PS1 0 0 PS12 0.5 1.4

PS2 0.2 0.6 PS13 0.5 1.6

PS3 0.3 0.7 PS14 0.5 1.8PS4-PS7 0.3 0.9 PS15 0.6 2

PS8-PS9 0.4 1.1 PS16 0.8 6

PS10 0.5 1.2 PS17 1.5 15PS11 0.5 1.3 PS18 45 45

It has got 45 rules, which are formed in similar way of (12)

and listed in Table VII. Command pitch angle is crisped by theCoG defuzzification method using (13).

TABLE VII

R ULES OF FUZZY LOGIC SYSTEM (METHOD 2)

β c* Δe

NL NS ZE PS PL

e ZE XXS S ZE S L

ZEP XS M ZEP M XL

XXS S L XXS L XXL

XS M XL S XL XXL

S L XXL S XXL XXL

M XL XXL L XXL XXL

L XXL XXL L XXL XXL

SL XXL XXL XXL XXL XXL

XXL XXL XXL XXL XXL XXL

V. SIMULATION R ESULTS

To investigate the effectiveness of the proposed methods,the wind turbine has been connected to the grid. The

evaluation has been performed by means of simulation and the

comparison of the control action and collective responses of

the conventional method (with PI controller) and the proposedmethods (with fuzzy logic controller) at the grid. The

simulation tool in this study is MATLAB/Simulink.

A fluctuating wind has been simulated, which causes wind

turbine to operate above rated wind incidents between 102s

and 160.2 s and below rated wind incidents for the rest time of 300 s period (Fig. 7a). The wind pattern has been adopted

from [1] and [8].

During below rated wind incidents, there is no pitch angle

generation by the conventional PI pitch angle controller (Fig.

7b), as there is no point of limiting the generator rotor speed.

So the wind turbine operates at highest possible efficiency, but

the output active and reactive powers have high fluctuations

due to abrupt variations in wind speed (Fig. 7c and 7d). This is

because wind power depends on the cube of the wind speed

and the input torque can not be controlled [8]. The controller

works only when the above rated wind incidents prevail

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Fig. 7. Evaluation of the proposed methods: (a) Wind speed, (b) Pitch angle, (c) Active power, (d) Reactive power, (e) Maximum energy function and (f)Smoothing function.

In this mode, the machine produces current at its maximum

rating. So, constant output power is ensured even there are

abrupt wind speed variations. The controller activates pitchactuation for shedding mechanical power to prevent generator

rotor from going above the control speed limit value.

On the other hand, the proposed methods work for

smoothing purpose during below rated wind incidents. As thecontrol strategy by the proposed method 1, a command valueis generated smaller than the reference output power with the

proper selection of correction factor by fuzzy reasoning. The

controller actuates pitch to limit the output power so that the

output power follows the command value. It means power

fluctuation minimization is achievable with the cost of some

output power drops. Fig. 7c shows that partially smoothingoutput power has been achieved during below rated wind

incidents. The reactive power absorbed (due to having

capacitive loads and being negative) by the generator is also

partially smoothed with the generation of partially smoothingactive power (Fig. 7d).

The proposed method 2 causes pitch actuation to limit the

output power to a target value ( Pg_tar ) during below rated

wind incidents. In Fig. 7c, it is seen that the proposed

controller sheds the output active power to ensure smoothing

output power of 0.7 p.u., as it falls into PS4 when there is no pitch angle generation for the first 40.5 s. We get smoothing

output power of 0.65 p.u in the next 57.6 s, smoothing output power of 0.8 p.u. between 161.4 s and 218.4 s and smoothing

output power of 0.6 p.u. onwards. This is how active power is

always shed to Pg_tar corresponding to the power stage itresides for smoothing the fluctuations. The reactive power absorbed by the generator is also automatically smoothed with

the generation of smoothing active power (Fig. 7d). The pitch

angle generation for both methods are shown in 7b.

The validity of the proposed methods in output power

smoothing has been carried out numerically by introducing the

concept of maximum energy function ( P max

) and smoothing

function ( P smooth), which are expressed as [8]

( )dt t P P t

g ∫ =

0max (14)

( )dt

dt

t dP P

t g

smooth ∫ =

0(15)

Fig. 8. Numerical validation of the proposed fuzzy logic controllers: (a)Maximum energy function and (b) Smoothing function.

Larger value of maximum energy function (P max

) in (14)means higher efficient operation of the wind turbine. Ascompared with the conventional method, maximum energyfunctions for both proposed methods drop slightly because thepitch angle remains fixed at 0

owhen conventional controller is

applied. Since the purpose of this work is to smooth the outputpower, a drop in the output power can not be avoided. But,this drop should be as minimum as possible for efficientoperation. Fig. 8a shows that the maximum power captured byusing the proposed methods 1 and 2 are less approximately by

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4.7% and 8.28% respectively than that by using theconventional method. We have achieved a great performancein this regard, where output power smoothing was achievedwith the cost of approximately 30% drop in output power [8].

On the other hand, if smoothing function (P smooth

) in (15) issmall, the output power fluctuation is small, which is theindication of good performance in smoothing output power.Fig. 8b shows that smoothing function drops to about 2/3 and

1/2 due to the application of the proposed methods 1 and 2respectively in the comparison with the conventional method.We have achieved a greater control on input torque too.

The proposed method 1 demonstrates the economic benefitby employing power storage system of smaller capacitybesides the controller for partial smoothing purpose. Theproposed method 2 ensures complete smoothing with a littlemore drop in output power than the proposed method 1. Itdemonstrates no requirement of compensation of output powerfluctuations by means of power storage system at least duringnormal operations ensuring greater economic benefits.

VI. CONCLUSIONS

This paper presents two methods of fuzzy logic system for

the pitch angle controller to smooth wind power fluctuations

during below rated wind incidents by dynamic pitch actuation.

The first method is based on the determination of thecommand output power based on the (Exponential MovingAverage) EMA with a proper selection of correction factor by

fuzzy reasoning to make output power to follow that command

value. The second method is based on the dynamic selections

of target output power values according to the wind incident

and limit power to obtain the target output. The performancesof the proposed fuzzy logic controller with both methods have

been compared with that of the conventional PI controller. The

results indicate that both of the proposed methods smoothoutput power fluctuations with significantly small drops of

output power as compared to that stated in the previous works.

However, the method 1 performs partial smoothing with only4.7% drop in output power demonstrating the economic

benefit by employing power storage system of smaller

capacity besides the controller for smoothing purpose. The

method 2 performs complete smoothing with 8.28% drop in

output power demonstrating greater economic benefits with norequirement of compensation of output power fluctuations by

means of power storage system at least during normal

operations.

VII. R EFERENCES

[1] R. M. Kamel, A. Chaouachi and K. Nagasaka, "Wind power smoothing

using fuzzy logic pitch controller and energy capacitor system for improvement Micro-Grid performance in islanding mode," Energy, vol.

35, no. 5, pp. 2119-2129, 2010.

[2] J. Cidras, A. Feijioo, and C. Carrillo, “Synchronization of asynchronouswind turbines,” IEEE Trans Power Syst , vol. 17, no. 4, pp. 1162–1169,

2002.

[3] T. Kinjo, T. Senjyu, K. Uezato, and H. Fujita, “Output Leveling of WindPower Generation System by EDLC Energy Storage System,” Elec Eng in Jpn, vol. 154, no. 4, pp. 34-41, 2006.

[4] T. Senjyu, T. Kinjo, K. Uezato, and H. Fujita, “Terminal Voltage and

Output Power Control of Induction Generator by Series and Parallel

Compensation Using SMES,” Elec Eng in Jpn, vol. 149, no. 3, pp. 15-

23, 2004.[5] S. M. Muyeen, S. Shishido, M. H. Ali, R. Takahashi, T. Murata, and J.

Tamura, “Application of energy capacitor system to wind power

generation,” Wind Ener , Vol. 11, no. 4, pp. 335-350.[6] R. Sakamoto, T. Senjyu, T. Kinjo, N. Urasaki, and T. Funabashi,

"Output power leveling of wind turbine generator by pitch angle control

using adaptive control method," in Int Conf Pow Syst Tech, 2004, pp.834-839.

[7] R. Sakamoto, T. Senjyo, T. Kaneko, N. Urasaki, T. Takagi, S. Sugimoto,

and H. Sekine, "Output Power Leveling of Wind Turbine Generator by

Pitch Angle Control Using H∞ Control," in IEEE Pow Syst Conf and Expo, 2006, pp. 2044-2049.

[8] T. Senjyu, R. Sakamoto, N. Urasaki, T. Funabashi, H. Fujita, and H.Sekine, "Output power leveling of wind turbine Generator for all

operating regions by pitch angle control," IEEE Trans Ener Conv, vol.

21, no. 2, pp. 467-475, 2006.[9] M. G. Gracia, M. P. Comech, J. Sallan and A. Llombart, “Modelling

wind farms for grid disturbance studies,” Renew Ener, vol. 33, no. 9, pp.

2109-2121, 2008.[10] SimPowerSystems – Model and simulate electrical power systems.

User’s guide. Natick (MA): The Mathworks Inc.; 2010.

[11] NIST/SEMATECH – e-Handbook of statistical methods. [Online].Available: http://www.itl.nist.gov/div898/handbook/index.htm

VIII. BIOGRAPHIES

Md. Ayaz Chowdhury received the bachelor degree in Electrical and Electronic Engineering from

Islamic University of Technology (IUT),

Bangladesh, in 2007. He worked from 2008 as a

Lecturer with International Islamic University

Chittagong (IIUC), Bangladesh. He has been

working as a Ph.D. student at Swinburne University

of Technology, Australia since 2009. His research is

mainly focussed on power system dynamics and

stability and applications of intelligent control and power compensating device in wind energy systems.

Nasser Hosseinzadeh (IEEE-M’86, CIGRE-APC1) received the Ph.D. degree in Electrical and

Electronic Engineering from Victoria University of

Technology, Australia in 2002. Earlier, he worked

as an Assistant Professor at Shiraz University inIran, as a Lecturer at Monash University Malaysia

and as a Senior Lecturer at Central QueenslandUniversity in Australia. He is currently with

Swinburne University of Technology, Melbourne

as a Senior Lecturer . His special fields of interest

include power system analysis and planning, wind

energy systems, power system stability, applications of intelligent control in

power engineering, and engineering education. Dr. Hosseinzadeh is a Member of IEEE and also is on the Australian Panel C1 (System Development and

Economics) of CIGRE.

Weixiang Shen (IEEE-M’02) received the Ph.D.

degree in Electrical and Electronic Engineering from

The University of Hong Kong, Hong Kong in 2002.

He worked as Lecturer at Department of Electrical

Engineering at Hefei University of Technology,China, in 1990 and Associate Professor in 1995. Hewas Visiting Scholar at University of Stuttgart,

Germany, from 1993 to 1994, Lecturer at Monash

University, Malaysia, from 2003 to 2008 andResearch Fellow at Nanyang Technological

University, Singapore, from 2008 to 2009. He is currently with Swinburne

University of Technology, Melbourne as a Senior Lecturer . His researchinterests include battery modelling and charging technology for electric

vehicles and renewable energy generation systems, power electronics, and

power system. Dr. Shen is a Member of IEEE.

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