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228 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 25, NO. 1, MARCH 2010

A Novel Scheme for Rapid Tracking of MaximumPower Point in Wind Energy Generation Systems

Vivek Agarwal , Senior Member, IEEE , Rakesh K. Aggarwal, Pravin Patidar, and Chetan Patki

Abstract—This paper presents a novel maximum power point(MPP) tracking (MPPT) algorithm for grid-connected wind energygeneration systems (WEGS). This is a rapid tracking algorithmthat uses the fact that the value of “β,” an intermediate variable,especially defined for the purpose, remains constant (=βMPP ) fora given WEGS at the MPP irrespective of the wind velocity. Thevalue of βMPP is known in advance. The algorithm works in twostages. In the first stage, it uses large steps to quickly drive theoperating point to lie within a narrow band with limits βmax andβmin . In the second stage, exact MPP is tracked using the “per-turb and observe” method. No extra hardware or measurements(sensors) are required compared to the existing algorithms. Hence,the cost is not increased. Application of the proposed algorithm

to an example WEGS shows that the time taken by the system toreach MPP is much smaller compared to most of the existing algo-rithms. A prototype matrix converter has been developed for gridinterfacing and theproposed MPPT schemehas been implementedin conjunction with Venturini and space-vector-modulation-basedswitching schemes. All the results of this study are presented.

Index Terms—Matrix converter (MC), maximum power pointtracking (MPPT) algorithm, space vector modulation (SVM),squirrel cage induction generator (SCIG), Venturini, wind energygeneration system (WEGS).

I. INTRODUCTION

THE DEMAND for electric energy is increasing rapidly.

Since the conventional fuels are depleting fast and theirprices are going up, the attention has shifted to nonconventional

energy sources, like wind, solar, fuel cell, etc. In this context,

wind is a particularly attractive option. Electric energy is gener-

ated from wind using a wind turbine and an electric generator.

The generated energy can be used either for standalone loads or

fed into the power grid through an appropriate power electronic

interface, such as a matrix converter (MC).

Different types of electric generators are used for the gener-

ation of electric energy from wind. These include the squirrel

cage induction generator (SCIG), the doubly fed induction gen-

erator (DFIG), and the synchronous generator (SG) [1]–[3]. Out

of these, the SCIG is most commonly used because of severaladvantages it offers, viz., it is robust, economical, involves low

maintenance cost, and is easy to control [4]. The work reported

in this paper is based on SCIG.

At a particular wind velocity, the amount of power generated

by the turbine depends upon the speed of the turbine, turbine

parameters, and the air density. The air density is usually as-

Manuscript received December 28, 2007; revised February 11, 2009. Firstpublished December 8, 2009; current version published February 17, 2010.Paper no. TEC-00501-2007.

The authors are with the Applied Power Electronics Laboratory, Departmentof Electrical Engineering, Indian Institute of Technology Bombay, Mumbai400 076, India.

Digital Object Identifier 10.1109/TEC.2009.2032613

Fig. 1. Turbine power versus turbine speed for different wind velocities.

sumed to be constant. Turbine parameters are determined by its

design and are constant. Therefore, for a fixed blade pitch angle

turbine, the output power of the turbine is mainly dependent

upon the turbine speed. Fig. 1 shows the nonlinear power–speed

characteristics of a turbine. The characteristics shift as the wind

velocity (V W ) varies. Each power–speed curve is characterized

by a unique turbine speed (ω∗

r ) corresponding to the maximum

power (P ∗) point (MPP) for that wind velocity [5]. This ef-

fectively means that for a given wind velocity, if the turbine

is rotated at (ω∗

r ), maximum power can be extracted from thewind.

Conventionally, the energy from the wind is extracted by

using a constant speed wind energy generation system (WEGS).

The extracted energy is converted into electric energy by using

an SCIG or DFIG and is supplied to the grid or a standalone load.

The main drawback of this system is its poor efficiency because

it cannot track the MPP [6], [7] as the wind velocity changes.

This situation is depicted by segment T–V–Q–U in Fig. 1. Let

the constant speed system be set to correspond to MPP “Q”

for a wind velocity of 9 m/s. This would result in the system

running at points U, V, and T forother wind velocities, which are

far away from the actual MPP points P, R, and S, respectively,

for the corresponding wind velocities. With the advent of highspeed, high power converters, variable-speed operation of the

WEGS has now become possible and the system can be made

to run at a speed corresponding to MPP for the current wind

velocity, i.e., the system, represented by Fig. 1, can run at P, Q,

R, and S. The amount of energy captured from the wind in this

case is much higher than a fixed speed system.

Several MPP tracking (MPPT) algorithms have been pro-

posed in the past [5]–[10], such as perturb and observe (P&O),

anemometer-based method, calculation-based method, fuzzy-

logic-based scheme, etc. In the P&O algorithm, the turbine

speed is varied in small steps and the corresponding change

in power is observed. Step changes are effected in a direction

0885-8969/$26.00 © 2009 IEEE

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AGARWAL et al.: NOVEL SCHEME FOR RAPID TRACKING OF MAXIMUM POWER POINT IN WIND ENERGY GENERATION SYSTEMS 229

Fig. 2. Block diagram of a typical grid-connected WEGS.

so as to move toward MPP [7], [9]. This process is continued

until MPP is reached. By using this algorithm, maximum power

corresponding to any wind velocity can be captured. But the

time taken to reach MPP is long and a considerable amount of

power loss takes place during the tracking phase.

In the anemometer-based MPPT algorithm, the wind velocity

is measured and a reference speed for the induction generator

(IG) corresponding to the MPP of the present wind velocity isset [8]. Although this is a fast MPPT scheme, the overall cost of

the system increases because anemometer is expensive. Fuzzy-

control-based scheme [6] is good, but is complex to implement.

The algorithm proposed by Wang and Chang [10] is independent

of the turbine characteristics and has good dynamic tracking

speed. However, this scheme also results in slow MPPT because

it needs to compute dV dc /dt for its control action.

Use of MPPT is not beneficial for capturing maximum power

in standalone applications [8], [11]. In fact, in this case, an

arrangement is also required to satisfy the reactive power de-

mand of the WEGS. In the grid-connected system, however, any

amount of power generated by the WEGS can be injected intothe grid. Hence, at any wind velocity, the system can be operated

at MPP to maximize the generation and utilization of power [4].

The block diagram of a typical grid-connected WEGS is shown

in Fig. 2.

When an SCIG with power converter [12]–[14] or DFIG with

rotor side control is used, the speed of the IG can be varied

over a wide range by changing the frequency at the generator

terminals [15]. However, this frequency may be different from

the grid. Hence, a power converter is needed to interface the IG

to the grid [16]. In the past, the commonly used configuration of

power converter for the WEGS was the back-to-back connection

of two power converters along with a large capacitor serving as

the dc link [4]. The main disadvantage of this configurationis the requirement of a bulky capacitor for the dc link, which

also reduces the life of the converter. The MC is an emerging

alternative to the two-stage ac–dc–ac power converter [17], [18].

The MC provides a single stage ac to ac conversion with the

control of output voltage, output frequency and input power

factor. It also eliminates the requirement of the bulky dc-link

capacitor, hence making the system compact [19]. Also, the

MC is inherently a bidirectional power converter.

In this paper, a new and fast MPPT algorithm is proposed,

which is much quicker than most of the existing schemes and

yet, it does not require any extra hardware. The algorithm drives

the system in twostages. In the first stage, large iterative steps are

Fig. 3. C P versus tip speed ratio curve.

used to move within a close range of MPP. In the second stage,

conventional P&O method is used to track the exact MPP corre-

sponding to the current wind velocity. Useof MC as the interface

between the grid and the WEGS is investigated. The operating

frequency of MC is governed by the MPPT scheme used. The

proposed MPPT scheme has been tuned in conjunction withboth Venturini and space vector modulation (SVM) switching

schemes. The control logic for the MC is implemented using

Texas Instrument’s DSP (TMS320F2812) [20]. All the details

of this study are presented in the subsequent sections of this

paper.

II. PROPOSED MPPT SCHEME

Theoutput power of the wind turbine is givenby thefollowing

equation [4]:

P = 0.5ρ C P AV 3w (1)

where P is the turbine output power (in watts), ρ is the air density(in grams per cubic meters), C P is the power coefficient (dimen-

sionless), A (πR2r ) is the cross-sectional area of the turbine (in

square meters), V W is the wind velocity (in meters per second),

and Rr is the radius of the turbine shaft. C P is a function of λand θ, and is given by [15]

C P (λ, θ) = 0.73

151

λi− 0.58 θ − 0.002 θ2.14

−13.2

e−18 .4/λi

(2)

where

λi = 1

λ− 0.02 θ

−0.003

θ3

+ 1−1

(3)

with

λ =ωr Rr

V w(4)

where θ is the turbine blade pitch angle, ωr is the turbine rota-

tional speed (in radians per second), and λ is the tip speed ratio.

The parameter C P signifies the component of wind energy,

which is converted to mechanical energy by the wind turbine.

Fig. 3 shows the C P versus tip speed ratio curve. As per

this plot, if the system operates at the peak point of the curve,

irrespective of the wind velocity, the power captured from the

wind is maximum. For this purpose, the turbine speed should


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be adjusted in such a way that the tip speed ratio corresponds to

MPP.

To determine the turbine speed corresponding to MPP for a

particular wind velocity, (1) is differentiated with respect to the

turbine speed and equated to zero assuming the air density and

turbine radius to be constants. This yields

∵ dP dωr

= 12

ρAV 3w dC P

dωr(5)

using dC P /dωr = (dC P /dλi )(dλi /dωr ), (5) can be rewritten

as

dP

dωr=

1

2ρAV 3

w

dC P

dλi

dλi

dωr. (6)

Differentiating (2) with respect to λi , keeping the blade pitch

angle θ constant, yields

dC P

dλi=

−110.23

λ2i

+2028.23

λ3i

−13.43ψ

λ2i

e−18 .40 /λi (7)

where ψ = 0.58θ + 0.002θ2.14

+ 13.2.Differentiating (3) with respect to ωr gives

dλi

dωr=

V w Rr

η(θ3 + 1) − 0.003σ

(V w η − 0.003Rωr )2

(8)

where σ = 0.02(1 + θ3 )θ and η = 1 + 0.00006θ + θ3 .

Using (5)–(7), we have

dP

dωr=

1

2ρAV 3

w

−110.23

λ2i

+2028.23

λ3i

−13.43ψ

λ2i

e−18 .4/λi

dλi

dωr. (9)

At MPP, dP/dωr = 0. Applying this condition to (9) providesthe value of turbine speed corresponding to the MPP (ωrM P P

),

as follows:

ωrM P P=

V wRr

2028.23η + σ(110.23 + 13.40ψ)

(θ3 + 1)(110.23 + 13.43ψ) + 6.08

. (10)

Putting θ = 0, ψ = 13.2, σ = 0, and η = 1 in (10) gives

ωrM P P=

V wRr

× [6.91] (11)

λM PP =ωrM P P

Rr

V w= 6.91. (12)

Using (2), (3), and (12) yields λiM P P and C P M P P as follows:

λiM P P≈ 7.05 (13)

C P M P P= 0.44. (14)

Substituting the value of V W from (11) and using values of

C P M P Pand A = πR2

r in (1) yields the power corresponding to

the MPP as follows:

P M PP = 0.5 × 0.44ρπR2r

Rr ωrM P P

6.90

3

. (15)

Rearranging (15) results in

P M PP = 2.10× 10−3

ρR5r ω

3rM P P . (16)

Fig. 4. (a) Power versus β curves for different wind velocities. (b) Turbinespeed versus β curves for different wind velocities.

Fig. 5. Turbine power and speed versus β curves (not to scale). The variousoperating sectors are shown in different shades.

At this point, a new variable β is introduced, which is defined

as β = ω3r

P . The value of β corresponding to MPP is given

by

β M PP = ω3rM P P

P M PP= 476.20

ρR5r

. (17)

The right-hand term of (17) involves quantities that are con-

stant for a particular wind turbine system and are known from

the specifications of the turbine. By substituting these values,

β M PP can be predetermined for a given system.

The turbine output power versus β curves for different wind

velocities areshown in Fig. 4(a). It is observed that themaximum

power increases with an increment of the wind velocity, but the

value of β at MPP, i.e., β M PP remains constant irrespective of

the wind velocity. This is an important observation and forms

the basis of the proposed MPPT algorithm. Turbine speed versus

β curves are shown in Fig. 4(b).Averaging the curves shown in Fig. 4(a) and (b) over the wind

velocity range, results in the set of curves shown in Fig. 5. The

entire operating region is divided into three sectors. Sector 1

is further divided into subsectors 1-A and 1-B as shown. It

should be noted that β M PP corresponds to the MPP only at the

junction of sectors 2 and 3. The junction of sectors 1-A and 1-B,

although also denoted by β M PP , does not represent MPP. The

current operating sector of the system can be identified using the

slopes of the turbine power versus β and turbine speed versus

β curves. The slopes of both the curves are negative in sector 1

and positive in sector 2. In sector 3, the power versus β curve

has a negative slope, whereas the speed versus β curve has a


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Fig. 6. Flow chart of the proposed algorithm.

positive slope. Based on these observations (see Fig. 5), a novel,fast MPPT algorithm is described next.

Proposed MPPT algorithm.

1) Measure the speed of the wind turbine (ωr k ) apart from

voltage, current, and frequency (f k ) at stator terminals of

the IG.

2) If the present frequency (f k ) at the stator terminals is

equal to the reference frequency (F k−1 ), calculate the

present turbine output power (P k ), β k , ∆P k = P k −P k−1 , ∆ωk = ωk − ωk−1 , and ∆β k = β k − β k−1 .

3) Identify the operating sector (see Fig. 5) depending upon

the value of ∆P k /∆β k and ∆ωk /∆β k . If both are nega-

tive then the sector is 1, if both are positive then the sector

Fig. 7. Basic block diagram of an MC.

Fig. 8. Basicblockdiagramof implementationof MCusing Venturinischeme.

is2,andif ∆P k /∆β k is negative but ∆ωk /∆β k is positive

then the sector is 3.4) If β k value (i.e., current value of β ) lies within the

“β M PP ±∆β ” band and the operating sector is either

2 or 3, set the reference frequency equal to the present

frequency, i.e., F k = f k .

5) If the current sector is 1 and β k > β M PP , set the refer-

ence frequency F k = f k + f min1 . If β k < β M PP , set the

reference frequency F k = f k + f min2 .

6) If the current sector is 2 and β k < β m in , set the refer-

ence frequency, F k = f k + (β M PP−β k )Gf , else set the

reference frequency, F k = f k + f min3 .

7) If the current sector is 3 and β k > β m ax , set the refer-

ence frequency, F k = f k + (β M PP−β k )Gf , else set the

reference frequency F k = f k − f min3 .8) Go to step 1) and start again. In this manner, continuously

track the maximum power at any wind velocity.

The flowchart corresponding to this algorithm is shown in

Fig. 6. The operating point may lie in any of the sectors shown

in Fig. 5. If the operating point is in sector 1, with β k > β M PP

(sector 1-B), the stator frequency is altered by f min1 to drive

the operating point to MPP. On the other hand, if β k < β M PP

(sector 1-A), stator frequency is altered by f min2 to attain MPP.

If the condition is β mi n < β k < β m ax , a frequency step size

of f min3 is used. The values of f min1 and f min2 are governed

by WEGS parameters and the wind velocity range at a given

location (average wind velocity has been used in this study).


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Fig. 9. Simulation results using Venturini scheme. (a) Grid voltage and current (magnified ten times) injected into the grid. (b) Line voltage at SCIG terminals.(c) Phase voltage at SCIG terminals. (d) Phase current at SCIG terminals.

Fig. 10. (a) Output voltage sector and (b) input current sector.

To begin with approximate values are used. Let point “ X ” be

the farthest point on the speed versus β curve (see Fig. 5).

Then, f min1 is chosen so as to drive the turbine speed from

ωr (X ) to ωr ( MPP). Similarly, f min2 is approximately obtained

by judging the difference between ωr (Y ) and ωr( MPP). Both

f min1 and f min2 might need some fine tuning at the time of

system installation f min3 is a very fine frequency step change

used to attain the MPP as precisely as possible. If the operating

point lies in sector 2 or 3 such that β k > β m ax or β k < β mi n , the

stator frequency change is decided by Gf (β M PP−β k ), where

Gf is defined as the frequency gain factor and is used to reduce

the number of steps required to reach the MPP. The values of

β m in and β m ax are tuned in such a way that the band across the

β M PP is sufficiently narrow so that the time taken to reach MPP

is minimum. At the same time, if (β ma x−β mi n ) is too narrow,

the system may oscillate between sectors 2 and 3 before enteringthe (β ma x−β mi n ) band. Hence, there is a tradeoff between the

stability of the system and the MPPT speed while selecting

the values of β m in and β ma x . It is practically impossible for the

system to attainthe exact β M PP point. Hence, some computation

error tolerance must be provided to the controller. ∆β represents

this small error tolerance around β M PP .

III. SIMULATION OF WEGS

The speed of the wind turbine is usually very low. Therefore,

if the turbine is directly connected to the IG, then the frequency

at the IG terminals will also be very low and for a given power

output, the current at the IG terminals will become very large.Due to this reason, the turbine shaft is connected to the IG

through a gear box, with the gear ratio adjusted in such a way

that maximum power can be extracted over a wide range of wind

velocity with reasonable frequency at the IG terminals.

The whole system, comprising of the wind turbine, gear box,

SCIG, and an MC is simulated in the MATLAB software. The

simulation is done for a 15-kW WEGS with the following pa-

rameters: for wind turbine: ρ = 1.225 kg/m3 and Rr = 2.5 m;

for IG: rs = 0.2147 Ω, rr = 0.2205 Ω, Ls = 0.991 mH, Lr =0.991 mH, Lm = 0.06419 H, P = 4, and J = 0.102 kg·m2 . The

wind turbine, which is mechanically coupled to the SCIG, is

simulated using (1), (2), (3), and (4). Simulation of the SCIG

is done using the d –q model [21], [22]. Each block is modeledseparately in MATLAB/Simulink. Special computations and al-

gorithms, such as the MPPT algorithm, are written as MATLAB

programs (functions). These functions are invoked by the main

Simulink program whenever required.

To extract maximum power corresponding to a given wind

velocity, the frequency at the terminals of the IG is adjusted in

such a way that IG runs at a speed corresponding to the MPP.

To interface the variable frequency terminals of the IG to the

fix frequency grid, an MC (see Fig. 7) is connected between

the SCIG and the grid. The implemented MC consists of nine

bidirectional switches with each input phase connected to each

output phase through a bidirectional switch.The control of MC is implemented using Venturini algorithm

and SVM technique discussed later in this paper. Both the ends

of the MC can operate at a different frequency and different

voltages. One end of the MC operates at a frequency required

to operate the wind turbine at MPP, while the other end of the

MC is connected to the grid that operates at grid frequency. The

reference frequency input to the MC is decided by the MPPT

algorithm. The MC is simulated in MATLAB/Simulink software

using bidirectional switches (devices).

The simulated MC was controlled using both the Venturini

and the SVM schemes. These two schemes for MC control are

briefly described next.


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Fig. 11. Simulation results using SVM scheme. (a) Grid voltage and current injected into the grid. (b) Line voltage at SCIG terminals. (c) Phase voltage at SCIGterminals. (d) Phase current at SCIG terminals.

Fig. 12. Typical curves during the tracking of the MPP using the proposed MPPT algorithm.

Fig. 13. Comparison of MPPT speeds of the proposed scheme and the con-ventional P&O method. In the example considered, the MPP is approximately5.3 kW for the given wind velocity.

A. Venturini Scheme

The block diagram of the implementation of Venturini algo-

rithm is shown in Fig. 8.

Fig. 14. (a) Laboratory prototype of the MC for grid interfacing. (b) Realiza-tion of a bidirectional switch. Nine such switches are used in the MC shown

in (a).


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Fig. 15. Experimental results using Venturini strategy. (a) Grid voltage and current injected into the grid. (b) Line voltage at SCIG terminals. (c) Phase voltageat the SCIG terminals. (d) Phase current at SCIG terminals (maximum power of 240 W injected into the grid corresponding to a wind velocity of 8 m/s).

Fig. 16. Experimental results using SVM strategy. (a) Grid voltage and current injected into the grid. (b) Line voltage at SCIG terminals. (c) Phase voltage atSCIG terminals. (d) Phase current at SCIG terminals (maximum power 270 W injected into the grid corresponding to a wind velocity of 8 m/s).

The duty cycle of each switch is given by [23]

mk j =T kj

T =

1

3

1 +

2V k V jV 2

m

(18)

for k = A,B,C , and j = a,b,c.

The results of MC for Venturini scheme are shown in Fig. 9.

B. SVM Scheme

The block diagram of the implementation of SVM scheme is

same as shown in Fig. 8. The only difference is in the methodof calculating the switching sequence and turn-ON time. The

switching sequence and turn-ON time of the different switches

in MC depend upon the present sector of the rotating input

and output vectors [24], [25]. Fig. 10 shows the output voltage

and input current sectors in their corresponding space vector

planes. The duty cycles of the various switches depend upon the

position of the output voltage and input current vectors in the

space vector planes.

The duty cycles are calculated using the sector information

and the following equations:

dαγ =

T α γ

T = mv sinπ

3−

θv ·

sinπ

3−

θi

(19)

dαδ =T α δ

T = mv sin

π

3− θv

· sin(θi ) (20)

dβ γ =T β γ

T = mv sin(θv ) · sin

π

3− θi

(21)

dβ δ =T β δ

T = mv sin(θv ) · sin(θi ) (22)

d0 =T 0T

= 1 − dαγ − dαδ − dβ γ − dβ δ . (23)

The results of MC for SVM scheme are shown in Fig. 11.The value of β for the example system considered is 3.97

from (17). Initially, the wind velocity of the system is assumed

to be 8 m/s and the algorithm tries to move the system toward

MPP. The time taken by the system to reach MPP is nearly 3 s.

At t = 4 s, the wind velocity changes from 8 to 11 m/s in a step-

wise manner [Fig. 12(a)] and the system tends to move toward

the new MPP corresponding to the wind velocity of 11 m/s.

Accordingly, the reference output frequency of the MC changes

with variable steps as shown in Fig. 12(b). The corresponding

change in output power is shown in Fig. 12(d). Fig. 12(c) shows

the variation of β during the tracking of the MPP. It can be con-

firmed from Fig. 12(c) that as the operating point tends toward


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MPP, the value of β approaches the constant value of 3.97

(for the example system considered), corresponding to the MPP

for any wind velocity. At t = 6 s, the wind velocity changes

from 11 to 8 m/s, and then, again, to 14 m/s at t = 9 s. The

corresponding variations of the different parameters (reference

frequency, power, etc.) during the tracking can be verified from

Fig. 12.

Fig. 13 shows the time taken by the P&O scheme and the

proposed scheme for tracking of the MPP for a wind velocity

of 10 m/s. Initially, the system is running at 20 Hz frequency.

At t = 3 s, the wind velocity changes in a stepwise manner

and both algorithms start tracking the MPP. The time taken by

the conventional P&O scheme to track the MPP is nearly 4 s,

while the proposed scheme takes approximately 1 s. Thus, the

proposed scheme tracks much faster and is able to capture more

energy from the wind during the transient tracking phase.

IV. HARDWARE RESULTS

Hardware implementation of a 1-kW prototype of WEGSwas carried out in the laboratory, as shown in Fig. 14(a). The

following parameters were used:

Wind turbine: ρ = 1.225 kg/m3 , Rr = 1.5 m.

IG: rs = 0.2147 Ω, rr = 0.2205 Ω, Ls = 0.991 mH, Lr =0.991 mH, Lm = 0.06419 H, P = 4, and J = 0.102 kg·m2 .

The bidirectional switch used for implementing the MC is

shown in Fig 14(b). The switch was realized using two antipar-

alleled power MOSFETs (IRFP450). A power diode (U10A60)

was connected in series with the power MOSFET in both the

paths to increase the reverse voltage blocking capability of the

bidirectional switch.

The wind turbine was simulated using a dc motor. The torqueof the dc motor is controlled to match that of a wind turbine for

a given shaft speed. The armature control is used for the shaft

speeds below rated speed and the field control is used for the

speeds above rated speed.

Besides the main power stage consisting of an MC, Fig. 14(a)

also shows the snubber circuits, driver circuits, and control cir-

cuits. The control and MPPT logic was implemented using the

DSP (TMS320F2812). The voltage level of the output signal

from the DSP is 3.3 V. This signal was fed into a noninverting

amplifier (OP-AMP TL084) to minimize the loading of DSP and

to step up the signal to 5 V level. These noninverting signals are

given to the gate driver IC (HPCL-3120) for driving the device

and to isolate the power and control circuits.The proposed MPPT algorithm is tested for a wind velocity

of 8 m/s, using both Ventruni and SVM algorithms to drive the

MC. The experimental results are shown in Figs. 15 and 16,

respectively.

V. SUMMARY AND CONCLUSION

A new MPPT algorithm for WEGS has been presented and

implemented on a grid-connected system. Comparison with the

existing schemes, such as the P&O method, shows that the new

MPPT algorithm provides much faster tracking, resulting in

more optimal usage of the source.

An MC has been used for interfacing the WEGS with the

power grid. The MC is a better alternative than the combination

of two back-to-back connected (ac–dc–ac) converters. The MC

facilitates the change of frequency and voltage at the generator

terminals and is able to maintain unity power factor at the grid

terminals. MC also satisfies the reactive power requirement of

the IG. Computer simulations have shown encouraging results

and it is felt that the proposed topology has good potential

for applications in distributed generation as well as standalone

systems. Thelatter will, of course, need an energystorage source

like a battery to take full advantage of the MPPT scheme.

A notable drawback of the proposed MPPT algorithm, how-

ever, is its dependence on the system parameters. This is the

focus of further research, which is being carried out by the

authors. These results will be presented in a future paper.

REFERENCES

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Vivek Agarwal (S’93–M’93–SM’01) received theBachelor’s degree in physics from St. Stephen’s Col-lege, Delhi University, Delhi, India, the integratedMaster’s degree in electrical engineering from In-dian Institute of Science, Bangalore, India, and thePh.D. degree from the Department of Electrical andComputer Engineering, University of Victoria, BC,Canada.

After a brief stint with Statpower Technolo-gies, Burnaby, Canada as a Research Engineerduring 1994–1995, he joined the Department of Elec-

trical Engineering, Indian Institute of Technology-Bombay, where he is cur-rently a Professor. His main field of interest is power electronics. He works onthe modeling and simulation of new power converter configurations, intelligentand hybrid control of power electronic systems, power quality issues, electro-magnetic interference (EMI)/electromagnetic compatibility (EMC) issues, andconditioning of energy from nonconventional sources.

He is an Associate Editor of the IEEE TRANSACTION ON POWER ELECTRON-ICS, a Fellow of IETE, and a Life Member of the Indian Society for TechnicalEducation.

Rakesh Kumar Aggarwal was born in Rajasthan,India, in 1983. He received the B.E. degree in electri-cal engineering from M. B. M. Engineering College,Jodhpur, India, in 2004 and the M.Tech. degree inpower electronics and power systems from Indian In-stitute of Technology-Bombay in 2007.

He is currently working as an Engineer in R&DDepartment at CA, India. His research interests in-clude power electronics, embeddedsystems, cryptog-

raphy, computer security, and secure coding.

Pravin Patidar received the B.Tech degree in elec-trical engineering from Samrat Ashok TechnologicalInstitute, Vidisha, India, in 2004 and the M.Tech. de-greefrom the Indian Instituteof Technology-Bombayin 2007.

He is currently working as a Product and TestEngineer in Cypress Semiconductor India Pvt. Ltd.His research interests include power electronics, non-

volatile and volatile memories, microcontrollers, andreliability of semiconductor devices.

Chetan Patki was born in Mumbai, India, in 1984.He received the B.E. degree in electrical engineeringfrom Mumbai University, Mumbai, in 2005 and theM.Tech. degree in electrical engineering from IndianInstitute of Technology-Bombay (IIT-B) in 2009.

He is currently working with Prof. Vivek Agarwalas a Project Engineer in the Department of ElectricalEngineering, IIT-B. His technical interests include

power electronics, electric drives, renewable energy,and power quality issues.

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