Performance Improvement with a Robust Self Tuned

Fuzzy Logic Controller for Generator Control in

Wind Energy System

Swagat Pati , K.B.Mohanty, Benudhar Sahu Department of Electrical Engineering, National Institute of Technology, Rourkela, India

swagatiter@gmail.com kbmohanty@nitrkl.ac.in benudharsahu@gmail.com

Abstract— In this paper a line excited cage generator is considered

which is connected with the grid through a bidirectional PWM converter- inverter system. The generator is controlled by indirect field oriented control (IFOC) scheme. Fuzzy logic controllers (FLC) are used for the control purpose. The first FLC is used in the outer speed loop to track the generator speed with the reference speed for maximum power extraction and the second and third FLCs are used in the inner current loops for control of active and reactive power. The FLCs use normalized values of error and change of error as their inputs. The outputs of the FLCs are again multiplied with gains to give the control signals. A trapezoidal membership function is taken for the error input and triangular membership functions are taken for change of error as well as output. Again a robust self tuned fuzzy logic controller (STFLC) scheme is used in place of the FLCs. In this scheme a tuning FLC (TFLC) is used to tune the output gain of the main FLC. The inputs to both the FLCs are normalized values of error and change of error. The output of the TFLC is the output gain of the main FLC. The main FLC is similar to the FLC as discussed in the previous scheme. In the TFLC triangular membership functions are used for all input as well as output variables. The performances of both the schemes are simulated and a comparison is given. The simulation work is done in MATLAB coding environment. Keywords- Induction Generator; Back to Back PWM converters; Indirect Vector control; FLC; Self Tuned FLC

I. INTRODUCTION

Wind energy is one of the most important and promising source of renewable energy all over the world, mainly because it is considered to be nonpolluting and economically viable. At the same time there has been a rapid development of related wind energy technology. However in the last two decades, wind power has been seriously considered to supplement the power generation by fossil fuel and nuclear methods. Normally induction machines are used for generation purpose in wind energy systems. But due to the coupling effect between active and reactive power the response becomes sluggish and the control becomes difficult and complex in case of induction generators. When induction machines are operated using vector control techniques, fast dynamic response and accurate torque control are obtained. All of these characteristics are advantageous in variable speed wind energy conversion systems (WECS). Squirrel cage generators with shunt passive or active VAR (volt ampere

reactive) generators was proposed in [3], which generate constant frequency power through a diode rectifier and line commutated thyristor inverter. A comparative study of fixed speed and DFIG during power system disturbances such as network voltage sags and three phase faults, as well as the possibility of network voltage instability is investigated in [4]. The performance of a DFIG driven by a wind turbine connected to large power systems is studied in [5]. Operation of several self excited induction generators connected to a common bus is analyzed in [6]. The control systems for the operation of indirect rotor flux oriented vector controlled induction machines for variable speed wind energy applications are discussed in [7]-[9]. Sensorless vector control scheme suitable to operate cage induction generator is discussed in [7]. In [8] cage induction machine is considered and a fuzzy control system is used to drive the WECS to the point of maximum energy capture for a given wind velocity. In [11] a stator flux oriented vector control scheme is discussed to decouple the active and reactive power generated from the variable speed wind generation system employing DFIG. A new control scheme using non-linear controller and FLC is employed for a variable speed grid connected wind energy system in [12] where as a vector control scheme for both supply side and machine converters is discussed and the independent control of active and reactive power is done in [13]. A FLC based speed control scheme is described in [14] employing feed-forward vector control scheme for performance improvement of an induction machine. Again a self tuned fuzzy controller is implemented for the performance improvement of a field oriented control of an induction motor in [15]. Several other fuzzy logic MRAS scheme have been developed in [16] - [20]. The induction machine is connected to the utility using back to-back converters. In this paper a variable speed wind turbine driven squirrel cage induction generator system with two double sided PWM converters is described. Fuzzy controllers are used to optimize efficiency and enhance performance. Again a self tuned fuzzy logic control scheme is implemented for performance enhancement purpose and a comparison is given. The control algorithms are evaluated by MATLAB simulation study.

2010 International Conference on Industrial Electronics, Control and Robotics

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II. INDUCTION GENERATOR MODEL

The basic configuration of a line excited i

generator is sketched in Fig.1. Normally the

interfaced with the grid through back-to-back PWM c

configuration.

.

Fig: 1 Basic block diagram of the wind energy syste

The sign convention applied in this model i

to that of the motor, i.e. the stator currents are posit

flowing towards the machine and real power and

power are positive when fed from the grid.

Stator voltages:

Vqs = Rs iqs +dt

dqsΨ

+ ωeΨds

Vds = Rsids +dt

ddsΨ

- ωeΨqs

Rotor voltages:

Vqr = Rr iqr +dt

dqrΨ

+ (ωe-ωr) Ψdr

Vdr = Rr idr + dt

ddrΨ

- (ωe-ωr) Ψqr

The flux linkages in these equations were calculated

Ψqs = Ls iqs + Lm iqr , Ψqr = Lr iqr + Lm iq

Ψds = Ls ids + Lm ids , Ψdr = Lr idr + Lm id

The final mathematical model for the squirrel cage

machine is given as

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

qr

dr

qs

ds

V

V

V

V

=( ) ( )

( ) ( )⎢⎢⎢⎢

⎣

⎡

+−−

−−+−−

+

−−+

rrrremmre

rerrmrem

mmessse

memsess

sLRLsLL

LsLRLsL

sLLsLRL

LsLLsLR

ωωωω

ωωωω

ωω

ωω

where ‘s’ is the laplace operator and ‘ωr’ is the rotor

speed.

induction

stator is

converter

em

is similar

ive when

d reactive

(1)

(2)

(3)

(4)

d from:

qs

ds

induction

⎥⎥⎥⎥

⎦

⎤

rL

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

qr

dr

qs

ds

i

i

i

i

(5)

electrical

For a singly fed machine as the cage motor

The machine dynamics is given as

m

m

turbineeB

dt

dJTT ω

ω

+=+

)(22

3qrdsdrqsme iiiiL

pT −=

The active and reactive power of the generat

qsqsdsds iViVp +=

qsdsdsqs iViVq −=

III. CONTROL STRUCTUR

A. Induction generator control scheme

The control scheme is shown in Fig.2. Indi

control scheme is implemented for high pe

of the cage generator. The key feature of fiel

is to keep the magnetizing current at a consta

T

Fig: 2 Control Structure

Thus the torque producing component o

adjusted according to the active power dem

dynamic response . With this assumption

formulation can be written as

ds

qs

r

rsl

i

i

L

R=ω

qsdr

r

me i

L

LPT ψ

22

3=

where ωsl is the slip speed and ψdr is the

linkage.

Vqr =Vdr = 0

(6)

(7)

or is given as

(8)

(9)

RE

irect field oriented

erformance control

ld oriented control

ant rated value.

of current can be

mand with a better

the mathematical

(10)

(11)

e d-axis rotor flux

186

The basic configuration of the system cons

line excited squirrel cage induction generator interfa

the grid through back-to-back PWM converters.

The converters are voltage controlled bidi

voltage source inverters having a dc link between th

rotor speed is fed back by a speed sensor which is th

with the slip speed to get the synchronous speed

further integrated to get the angular displacement

which the unit vectors are generated.

The controller FLC-1 makes the rotor speed

the reference speed such that maximum power can be

from the wind by the wind turbine. The controlle

controls the direct axis rotor current such that t

magnetizing flux remains constant to get better

response.

The controller FLC-3 controls the quadra

stator current such that the given active power deman

met.

B. Fuzzy Logic Controller Design

The structure of the fuzzy logic controllers is

Fig. 3. FLC1 uses normalized values of speed e

change of speed error as its inputs. Ke and Kce are

scaling factors which are chosen to normalize the e

change of error respectively. The output of FLC1

multiplied with output scaling factor Ko to give contr

iqs*.

FLC2 uses normalized values of direct ax

current error and change of direct axis current err

inputs and it gives control output uds*. FLC3 takes no

values of quadrature axis current error and ch

quadrature axis current error as its inputs and gives

and uqs* when added with (-ωeψqs) and (ωeψds) res

give control voltages vds* and vqs

*.

Fig: 3 Block diagram of fuzzy controller

Table-1

Input error membership functions are s

Fig.4(a). The input error fuzzy sets use both triang

trapezoidal membership fuctions, which are foun

optimum. Triangular membership functions as s

Fig.4(b) are used for change of error input. Th

sists of a

aced with

irectional

hem. The

hen added

which is

θe from

d to track

captured

er FLC-2

the rotor

dynamic

ature axis

nd can be

s given in

error and

the input

error and

is again

rol output

xis stator

ror as its

ormalized

hange in

uqs*. uds

*

spectively

hown in

gular and

nd to be

hown in

he output

membership functions are shown in Fig.4(c)

the FLC algorithm is given in Table-1

Mamdani type fuzzy inferencing is used

constants, membership functions, fuzzy se

output variables, and the rules used in the

by trial and error to obtain optimum drive pe

study, the centroid method of defuzzification

(a)

(b)

(c)

Fig: 4 Membership functions for

(a) input membership functions for error (b) infunctions for change in error (c) output memb

C. STFLC design

The robust self tuned fuzzy logic controller

is used in place of the FLCs. In this sche

(TFLC) is used to tune the output gain of the

Fig: 5 STFLC gain tuning sch

. The rule-base for

. For this study

d. The values of

ets for input and

study are selected

erformance. In this

n is used.

FLC

nput membership bership functions

r (STFLC) scheme

eme a tuning FLC

e main FLC.

heme

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187

The inputs to both the main FLC and the Tnormalized values of speed error and change of speThe output of the TFLC is the gain updating factor α wa value between 0 and 1.When α is multiplied with the output gain of the STFLC. The main FLC is simiFLC as discussed in the previous subsection.. The gamechanism of the STFLC is shown in Fig 5. In thtriangular membership functions are used for all inpuas output fuzzy sets. With a view to improving thcontrol performance, we use the rule base in Tacomputation of α.

Further modification of the rule base for ‘αrequired, depending on the type of response the controdesigner wishes to achieve. It is very important to notrule base for computation of ‘α’ will always be depethe choice of the rule base for the controller. Any sichange in the controller rule base may call for changrule base for ‘α’ accordingly.

The input and output membership functions for the Tgiven in Fig:6. For the designing of the TFLC .Mamfuzzy inferencing is used. The values of membership ffuzzy sets for input and output variables, and the rulethe study are selected by trial and error to obtain optimperformance. In this study the centroid medefuzzification is used.

Table-2

(a)

(b)

Fig: 6 Membership functions for TFLC(a) input memfunction, (b) output membership function

TFLC are eed error. which has Ko gives

ilar to the ain tuning he TFLC ut as well he overall ble-2 for

’ may be ol system te that the endent on ignificant ges in the

TFLC are dani type functions, es used in

mum drive ethod of

mbership

IV. SIMULATION RESULT

The drive system was first simulated w

controllers with different operating conditi

change in the reference speed and step c

torque and some sample results are presente

section.

A step change in command speed f

1690 rpm is given at t = 2.5sec. which con

and again returns to the previous value.

controller the machine takes around 0.11

steady state but with self tuned fuzzy logic

approximately 0.065 sec. to achieve steady s

seen clearly in Fig.7

.(a)

(b)

Fig: 7 Speed response with (a) FLC (b) STFLC

speed command

In Fig. 8 it can seen that due to a ch

command there is no variation in active popower, but the reactive power response wsluggish than with STFLC. Again a step torque from 10 Nm to 15 Nm is given aresponses are shown . The turbine torque is kNm till t = 2 sec. At t = 2 sec. the turbine tor15 Nm and at t = 3 sec. again the turbine to10 Nm.

(a)

TS

with Fuzzy logic

ions such as step

change in turbine

ed in the following

from 1890 rpm to

ntinues for 0.5sec.

With fuzzy logic

15sec. to achieve

controller it takes

state which can be

for step change in

hange in the speed ower and reactive with FLC is more change in turbine

and the simulation kept constant at 10 rque is increased to orque is restored at

188

(b)

Fig: 8 Active power and reactive power response with (b) STFLC

(a)

(b)

Fig: 9 Speed response for a change in turbine torque wFLC (b) STFLC

(a)

(b)

Fig: 10 active and reactive power responses for a step turbine torque with (a) FLC (b) STFLC

(a) FLC

with (a)

change in

The speed response of the machine due

turbine torque is given in Fig: 9. In fig: 9 i

the controller is made on at t = 1 sec. Before

starts up in open loop. From Fig: 10 we can see that the active

increases with the increase in turbine torquevariation in reactive power with STFLC than

(a)

(b)

Fig: 11 Power factor response with (FL

For which there is an improvement in theSTFLC scheme which is justified form Fischeme the power factor varies from 0.62 change in turbine torque, Where as in thepower factor varies from 0.58 to 0.72.

(a)

(b)

Fig: 12 Line current response with (a)FLC (b

e to the change in

it can be seen that

e that the machine

e power generation e, but there is less in FLC scheme.

LC) (b) STFLC

e power factor with ig: 11. In STFLC to 0.745 with the

e FLC scheme the

) STFLC

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189

V. CONCLUSION

The self excited induction generator system was simulated

with both FLC and STFLC controllers in MATLAB . Both the

controllers improve the responses of the generator system.

While the generator system with FLC achieves steady state in

0.02 sec, with STFLC the system achieves steady state in

0.013 sec, which makes the STFLC faster controller than the

FLC. During the sudden change in turbine torque the STFLC

gives a better performance in reactive power control than the

FLC due to which the power factor in case of STFLC control

becomes better than that with FLC control. This concludes

that the STFLC has an overall better performance than the

FLC.

APPENDIX

Symbols

Vas, Vbs, Vcs = Three phase supply voltages

Vdss, Vqs

s = d- q axis voltages in stationary reference frame

Vds, Vqs = d-q axis voltages in synchronously rotating reference

frame

Rs, Rr = stator and rotor resistances

Ls, Lr = stator and rotor inductances

Lm = magnetizing inductance

Ψds, ψqs, ψdr, ψqr = stator and rotor flux linkages

ωe = synchronous speed(electrical)

ωr = rotor electrical speed

ωm* = reference rotor speed (mechanical)

Te = electromagnetic torque

Tturbine = prime mover torque

J = moment of inertia

B = frictional damping coefficient

ωsl = slip speed

ωm = rotor mechanical speed

P = pair of poles of the machine

p = active power

q = reactive power

Machine parameters

3 hp, 3Ф, 220V, 60Hz, P=4, Rs = 0.435Ω, Rr = 0.816Ω, Xls = 0.754Ω,

Xls=Xlr, Xm = 26.13Ω, J = 0.089 kg-m2, B = 0

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