Application of an Adaptive Neuro-Fuzzy Controller for

Speed Control of Switched Reluctance Generator Driven

by Variable Speed Wind Turbine

S. M. Muyeen and Ahmed Al-Durra

Electrical Engineering Department

The Petroleum Institute

P.O. Box 2533, AbuDhabi, UAE

s.m.muyeen@ieee.org and aaldurra@pi.ac.ae

Hany M. Hasanien

Electrical Power and Machines Department

Ain Shams University

Cairo 11517, Egypt

hanyhasanien@ieee.org

Abstract— This paper presents the application of an adaptive

neuro-fuzzy controller (ANFC) for speed control of the switched

reluctance generator (SRG). In this study, the SRG is driven by a

variable-speed wind turbine and connected to the grid through

an asymmetric half bridge inverter, DC-link, and DC-AC

inverter system. Speed control plays an important role in

variable-speed operation of the SRG to ensure maximum power

delivery to the grid for any particular wind speed. The

effectiveness of the proposed ANFC is compared with that of the

conventional proportional-Integral (PI) controller. Detailed

modeling and control strategies of the overall system are also

presented. The validity of the proposed system is verified by the

simulation results using the real wind speed data measured at

Hokkaido Island, Japan. The dynamic simulation study is

performed using the laboratory standard power system simulator

PSCAD/EMTDC.

Keywords— Adaptive neuro-fuzzy controller, asymmetric half

bridge inverter, switched reluctance generator, variable-speed wind

turbine.

I. INTRODUCTION

HE perpetual increasing concerns to environmental issues

and depletion of fossil fuel demand the search for more

sustainable electrical sources. Wind energy has already shown

great potential to become the leader in renewable power

generation, considering its striking growth rate in the last few

years. The Global Wind Energy Council is predicting the

global wind market to grow by over 155% from the current

size reaching 240 GW of total installed capacity by 2012 [1].

The variable-speed wind turbine generator system (WTGS)

has recently become more popular than that of fixed-speed and

holds 60% market share [2]. The doubly fed induction

generator (DFIG), wound field synchronous generator

(WFSG), and permanent magnet synchronous generator

(PMSG) is currently used as variable-speed wind generators.

Besides the aforementioned classical machines used in

variable-speed operation of WTGS, the switched reluctance

generator (SRG) has some superior characteristics suitable for

wind power applications. The SRG is a doubly salient, singly-

excited generator. It has an unequal number of salient poles on

both the rotor and the stator, but only one member (usually the

stator) carries windings, and each two diametrically poles

usually form one phase. The rotor has no winding, magnets, or

cage winding and is built up from a stack of salient-pole

laminations. The SRG possesses many inherent advantages

such as simplicity, robustness, low manufacturing cost, high

speed, and high efficiency [3]-[6]. It is also possible to operate

the SRG in variable-speed mode. To date, these applications

include sourcing aerospace power systems [7], automotive

applications [8], [9], hybrid vehicles [10], and wind turbine

applications [11]-[15]. The aerospace and automotive

applications are generally characterized by high speed

operation. The wind energy application is characterized by

low speed, high torque operation. In [11], the advantage of the

SRG for wind energy application was reported well, though

the control strategy was unfocused. The one phase reluctance

generator was proposed for wind energy conversion in [12]. In

[13], the grid interfacing of wind energy conversion system

was not considered. The authors reported the extension of [13]

into [14] considering grid interfacing and buck converter

based topologies for generator side control of SRG. In a recent

work [15], sensorless control of SRG has been focused though

the overall control strategy of SRG-WTGS has not been

presented.

In this paper, detailed modeling and suitable control

strategies are developed to operate the SRG at the variable -

speed mode under randomly varying wind speed conditions.

For supplying power to the SRG, a voltage source topology is

preferred and thus adopted in this study, which gives well

defined voltages over semiconductors and SRG-phases. The

premise of a voltage source topology implies a unipolar DC-

link voltage with a relatively large DC-link capacitor as an

energy buffer. The asymmetric half bridge inverter based on

hysteresis control is considered herein for the generator side

control of the SRG. It needs to mention that speed control is

very important for variable-speed operation of the SRG. An

adaptive neuro-fuzzy controller (ANFC) is considered to

control the switching on angle of the SRG to run the generator

at the optimum speed of variable-speed wind turbine that

T

Modern Electric Power Systems 2015 - MEPS’15 Wroclaw, Poland - July 6-9, 2015 www.meps15.pwr.edu.pl

ensures the maximum power extraction from the wind. This is

one of the salient features of this study. The effectiveness of

the proposed ANFC is then compared with that of the

conventional proportional-integral (PI) controller. The grid

interfacing is taken into consideration for the wind turbine

driven the SRG. To validate the effectiveness of the control

strategy, real wind speed data measured at Hokkaido Island,

Japan, is used in the simulation analyses. The simulation is

performed using PSCAD/EMTDC.

II. WIND TURBINE MODELING

The mathematical relation to the mechanical power

extraction from the wind can be expressed as follows [16]-

[24]:

2 30.5 ,M p WP C R V W 　 (1)

where PM is the extracted power from the wind, is the air

density [kg/m3], R is the blade radius [m], Vw is the wind speed

[m/s], and Cp is the power coefficient which is a function of

both tip speed ratio, , and blade pitch angle, [deg]. Cp is

expressed by the following equations [23]:

2 0.17, 0.5 0.02 5.6pC e (2)

3600,

1609

m

W

R R

V

　 (3)

where m is the rotational speed [rad/s]. Maximum power

point characteristics of wind turbine is given in Fig. 1.

CR

R0.5Pp_opt

3

opt

r2

max

(4)

From Eq. (4), it is clear that the maximum generated power

is proportional to the cube of rotational speed [24].

III. SRG MODELING INCLUDING THE INVERTER

The switched reluctance machine is operated in the

generating mode by positioning phase current pulses during

periods where the rotor is positioned such that the phase

inductance is decreasing. This occurs immediately after the

rotor and stator poles have passed alignment. Normally, in this

generator mode, the machine obtains its excitation from the

same voltage bus that it generates power to. The excitation

energy plus additional generated energy is returned to the dc

bus. The control key is to position the phase current pulses

precisely timed [25]. Fig. 2 illustrates a three phase SRG

inverter system with two controllable power semiconductor

switches and two diodes per phase, and this circuit is called an

asymmetric half bridge inverter for a 3-ph SRG.

The model of the SRG for dynamic analysis is composed of

set of phase circuit and mechanical differential equations. In

integrating these equations, the problem centers on handling

the data (flux-linkage/angle/current) used to describe the

magnetic nature of the SRG [26]. In this study, the

magnetization curves of the SRG are derived from the

measured data. The magnetization characteristics are extended

using the cubic spline interpolation algorithm to cover the

interval of rotor angles between the unaligned and the aligned

positions as shown in Fig. 3. The co-energy curves are

calculated from the following equation by applying the

trapezoidal rule in numerical integration [26]:

i

0

constθdii,Ψi),(W (5)

The static torque curves of the SRG are computed by

numerical differentiation of the co-energy using the following

equation:

constiθ

iθ,Wiθ,T

(6)

The previous characteristics data are carried out using

MATLAB toolboxes [27]. These characteristics are stored in

the form of look-up tables and used in the laboratory standard

power system simulator PSCAD/EMTDC.

The generator under study is a three phase, 6/4 SRG, and

the rated power is 48 kw at 3000 rpm. The phase resistance is

0.05 Ω, the machine inertia is 0.05 kg.m2, the supply voltage is

240V and all the parameters are illustrated in Appendix.

Fig. 2. Asymmetric half bridge inverter for a three-phase SRG.

0 50 100 150 200 250 300 350 400 4500

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Current (A)

Flu

x l

ink

ag

e (

Wb

)

aligned position theta=0 deg

theta=5 deg theta=10 degtheta=15 deg

theta=20 deg

theta=25 deg

theta=35 deg

theta=40 deg

unaligned position theta=45 deg

Fig. 3. Phase flux-linkage as a function of current and rotor position.

Fig. 1. Wind turbine characteristics for variable-speed operation

0.4 0.6 0.8 1.0 1.20.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Tur

bine

Inp

ut P

ower

[pu]

Rotor Speed[pu]

13m/s

12m/s

11m/s

10m/s

9m/s8m/s

7m/s6m/s

Locus of maximum

captured power

IV. CONTROL TECHNIQUES OF THE SRG INVERTER

The control block diagram for the asymmetric half

bridge inverter to generate gate pulse signals is shown in Fig.

4. The reference signal is determined from the maximum

power point tracking (MPPT) algorithm as explained in Sect.

II. The conduction signals are generated according to the logic

explained as follows:

The applied voltage (V) is positive during the

conduction period, which is the difference between

the switching off angle θoff and the switching on

angle θon.

V is negative from θoff until the extinction angle θext

which represents the angle corresponding to zero

phase current.

Otherwise, V equals zero.

The SRG rotor angular position, r, is shifted by 30 degree in

each phase for the 6/4 SRG. The hysteresis controller works

well to generate the optimal firing angle for the inverter in

order to maximize the output power of the generator according

to the reference signal. In this study, the control technique is

done using the ANFC in compared with that of the

conventional PI controller to verify the effectiveness of the

proposed ANFC.

a) PI Controller

The optimum speed is maintained by controlling the

switching on angle, on, as shown in Fig. 5, where r_opt is the

optimum rotational speed determined from MPPT. r_opt is

compared with the generator speed ωr to yield the speed error

e(t). This error signal is the input of the PI controller. The

output of the controller is a signal representing the incremental

change in the switching on angle Δθon(t).

The equation of the PI controller can be written as follows:

dtteKteKt ipon ).()(.)( (7)

where Kp is the proportional gain, and Ki is the integral gain.

The output signal of the PI controller Δθon(t) after being

rescaled is used to modulate the generator phase switching on

angle. The advanced switching-on angle θon new can be written

as follows:

)(.)( tkt ononinitialonnew (8)

where θon initial is the initial switching on angle (5°), and k is a

constant.

b) ANFC

In this study, the proposed neuro-fuzzy controller presents a

fuzzy logic controller with self tuning scaling factors based on

artificial neural network structure, as shown in Fig. 6 [28].

Firstly, the fuzzy logic control rules are described then the

neural networks (NN) architecture is introduced to self tune

the output scaling factor of the controller. The actual generator

speed ω(t) is compared with the reference speed ωref to yield

the speed error e(t). The incremental change of speed error

∆e(t) can be expressed as follows:

)1()()( tetete (9) The proposed controller has two input scaling factors of gains

Ge and G∆e and also one output scaling factor of gain G∆u. The

output signal of the input scaling factors can be written as

follows:

eN Gtete ).()( (10)

eN Gtete ).()( (11)

The output signals of the input scaling factors eN and ∆eN are

considered to be the inputs of the fuzzy logic controller (FLC).

The output signal of FLC ∆uN is the input of the output scaling

factor. The NN structure has two inputs e(t) and ∆e(t). The

output signal α of NN is used to fine tune the output scaling

factor. The only output signal of ANFC, u(t), can be written as

follows:

uN Gtutu .).()( (12)

For the FLC, the membership functions are defined off-line,

and the values of the variables are selected according to the

behavior of the variables observed during simulations. The

selected fuzzy sets for the FLC are shown in Fig. 7. The

control rules of the FLC are represented by a set of chosen

fuzzy rules. The designed fuzzy rules used in this study are

given in Table I. The fuzzy sets have been defined as: NB,

negative big, NS, negative small, Z, zero, PS, positive small,

and PB, positive big, respectively. In this study, Mamdani’s

max-min (or sum-product) method is used for the inference

mechanism [29]. The center of gravity method is used for

defuzzification to obtain un. [29].

Fig. 6. Block diagram of ANFC.

Fig. 4. Control block diagram for the SRG inverter

Gate Pulses

Conduction

Signals

r

VphIph

Popt

on

off

Pref

Fig. 5. Switching on angle control block diagram

PI Controller

r_opt

Δon r

Fig. 7. Fuzzy sets and their corresponding

membership functions

TABLE I

FUZZY RULE TABLE

un en

NB NS ZO PS PB e n

NB PB PB PS PS ZO

NS PB PS PS ZO NS

ZO PS PS ZO NS NS

PS PS ZO NS NS NB

PB ZO NS NS NB NB

On the other hand, for the NN structure, a three layer

feedforward neural structure with 2 × 3 × 1 (2 inputs, 3

hidden, and 1 output neuron) is used in this study, as shown in

Fig. 8. The input nodes were selected as equal to the number

of input signals and the output nodes as equal to the number of

output signals. The number of hidden layer neurons is

generally taken as the mean of the input and output nodes. The

selection of number of hidden layers, and the number of

neurons in each hidden layer was performed by trial and error

method which is the most commonly used method in the NN

architecture design. In this NN structure, three hidden neurons

were selected. The inputs consist of the speed error e(t), and

the change in speed error ∆e(t). These inputs are chosen based

on the system stability. The NN structure is based on the

Widrow-Hoff adaptation algorithm. The Widrow-Hoff delta

rule is used to adapt the adaline’s weight vector [30].

The output of the NN is the signal α which has certain

value between -1 and +1.

The activation function of the nodes in the hidden layer is:

x

x

e

exf

1

1)( (13)

The activation function of the output layer neuron is chosen to

be as follows:

xe

xh

1

1)( (14)

The output signal of ANFC u(t) is used to modulate the motor

phase switching on angle. The advanced switching-on angle

θon new can be written as follows:

uoninitialonnew (15)

In addition, the NN structure can be established to fine tune

the input scaling factors but the controller will be very

complex and also its effect on transient stability is low.

Therefore, in this study, one NN structure is used to fine tune

the output scaling factor.

Fig. 8. Block diagram of the NN structure.

V. CONTROL OF THE GRID SIDE INVERTER

The control block diagram for the grid side inverter is

shown in Fig. 9. It is based on the cascaded control scheme.

The dq quantities and three-phase electrical quantities are

related to each other by the reference frame transformation.

The angle of the transformation is detected from the three

phase voltages (va,vb,vc) at the high voltage side of the grid

side transformer.

The dc-link voltage can be controlled by the d-axis current. On

the other hand, the reactive power of the grid side inverter can

be controlled by the q-axis current. The reactive power

reference is set in such a way that the terminal voltage at high

voltage side of the transformer remains constant. The

triangular signal is used as the carrier wave of the pulse width

modulation (PWM) operation. The carrier frequency is chosen

1000 Hz.

VI. MODEL SYSTEM

The model system used for the dynamic analysis of VSWT-

SRG is shown in Fig. 10. Here one SRG is connected to an

infinite bus through the asymmetric half bridge inverter, DC-

link capacitor, grid side inverter, transformer, and double

circuit transmission line. The system base is 48 kVA.

VII. SIMULATION RESULTS

Simulation has been done by PSCAD/EMTDC [31]. The

dynamic characteristic of VSWT-SRG is analyzed under wide

range of wind speed variation which is a real data measured in

Hokkaido Island of Japan, as shown in Fig. 11. One of the

control objectives is to maximize the wind power capture by

adjusting the rotor speed of the wind turbine according to the

wind speed variation, provided that the captured power should

Fig. 10. Model System

bus

V=1 50Hz, 48kVA Base

P=1.0

V=1.0

0.1+j0.6

0.1+j0.6

j0.1

SRG

CB 0.13/6.6kV

~ -

CB

DC

abc

dq PWM

Igrid a,b,c

PLL θt

Id

Iq

PI-5

PI-2

Vd*

Vq*

Inverter

Switching

Signals

PI-4

+

- +

-

Vdc

PI-1 Vdc

*

+

Vgrid a,b,c

abc

dq

-

V*a,b,c

I*d

G1

1+T1s

1+T2s

+

-

V*grid

Vgrid

Q*grid

Qgrid

+

-

G2

1+T3s

1+T4s

I*q

PI-3

Fig. 9. Control block diagram for the grid side inverter

not exceed the rated power of the SRG. Fig. 12 shows the

rotor speed of the SRG and the optimum rotor speed

calculated from MPPT when the PI controller of gains kp = 0.2

and ki =0.01 is used.

Figure 13 shows the rotor speed of the SRG and the optimum

rotor speed when ANFC is used. By inspection of the last two

figures, it can be noted that the dynamic response of the SRG

when ANFC is used, is better than that when the conventional

PI controller is used. ANFC ensures excellent speed reference

tracking. The responses of real and reactive power at the grid

side inverter when using ANFC are shown together in Fig. 14.

The terminal voltage at the grid side is maintained constant as

shown in Fig. 15. The excitation switching on angle response

is shown in Fig. 16. The response of DC-link voltage is shown

in Fig. 17. From the simulation responses, it is seen that the

proposed control system is well suitable for wind power

application.

Fig. 11. Wind Speed

Fig. 12. Rotor Speed using the PI controller

Fig. 13. Rotor speed of the SRG using ANFC

Fig. 14. Real and reactive power of the grid-side inverter

Fig. 15. Terminal voltage of the grid

Fig. 16. Excitation switching on angle

Fig. 17. DC-link voltage

VIII. CONCLUSIONS

Application of the SRG has been presented in this paper for

the variable-speed operation of the wind power generation

system. The detailed modeling and control strategies of

VSWT-SRG including the generator side asymmetric half

bridge inverter and the grid side inverter have been presented.

The proposed control strategy can ensure maximum power

delivery to the grid and also supply the necessary reactive

power to maintain the terminal voltage of the grid constant. To

control the switching on angle of the SRG, an ANFC is

adopted which gives excellent dynamic performance in

compared with the conventional PI controller. This feature is a

new incorporation in the field of variable-speed wind power

generation system where the rotor speed of the SRG is needed

to control to extract maximum power from the wind. Finally,

it has been concluded that the proposed control system is well

suitable with variable-speed wind turbine driving the SRG.

REFERENCES

[1] The Global Wind Energy Council, “Global wind 2007 report,” Second

Edition, May 2008. [2] F. V. hulle, “Large scale integration of wind energy in the european

power supply analysis, issue and recommendations,” EWEA, Tech.

Rep., December 2005. [3] T. J. E. Miller,“Switched reluctance motors and their control”, Oxford

University Press, 1993.

0 100 200 300 400 500 6000.0

0.2

0.4

0.6

0.8

1.0

1.2

Term

inal V

oltag

e[pu

]

Time[sec]

0 100 200 300 400 500 6000

2

4

6

8

10

12

14

16

Excit

ation

On

Angle

[Deg

ree]

Time[sec]

0 100 200 300 400 500 6000

50

100

150

200

250

DC-L

ink V

oltag

e [V

]

Time[sec]

0 100 200 300 400 500 600-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Real

and

Reac

tive

Powe

rs

of G

rid S

ide In

verte

r[pu]

Time[sec]

0 100 200 300 400 500 600

9.5

10.0

10.5

11.0

11.5

12.0

12.5

Wind

Spe

ed [m

/sec

]

Time[sec]

0 100 200 300 400 500 6000.70

0.75

0.80

0.85

0.90

0.95

1.00 Rotor Speed of SRG Optimum Rotor Speed

Roto

t Spe

ed [p

u]

Time[sec]

0 100 200 300 400 500 6000.70

0.75

0.80

0.85

0.90

0.95

1.00

Roto

t Spe

ed [p

u]

Time[sec]

Rotor Speed of SRG Optimum Rotor Speed

[4] Hany M. Hasanien “Steady state performance of switched reluctance

generator” Journal of Electrical Engineering, University Politehnica, Timisoara, Romania, vol. 8, no. 1, pp 53-60. April 2008.

[5] Hany M. Hasanien, S. M. Muyeen, and Junji Tamura, “Torque ripple

minimization of axial laminations switched reluctance motor provided with digital lead controller”, Energy Conversion and Management, vol.

51, issue 12, pp. 2402-2406, Dec. 2010.

[6] Eyhab El-Kharashi, and Hany M. Hasanien, “Reconstruction of the switched reluctance motor stator”, Journal of Electrical Engineering,

Slovakia, vol. 63. no. 1, pp. 1-10, January 2012.

[7] D. E. Cameron and J. H. Lang, “The control of high-speed variable reluctance generators in electric power systems,” IEEE Transactions on

Industry Applications, vol. 29, no. 6, pp. 1106–1109, Nov./Dec. 1993.

[8] M. Besbes, M. Gasbi, E. Hoang, M. Lecrivian, B. Grioni, and C. Plasse, “SRM design for starter-alternator system,” in Proc. Int. Conf. Electric

Machines, 2000, pp. 1931–1935.

[9] C. Ferreira, S. R. Jones, W. Heglund, and W. D. Jones, “Detailed design of a 30-kw switched reluctance starter/generator system for a gas turbine

engine applications,” IEEE Transactions on Industry Applications, vol.

31, no. 3, pp. 553–561, May/June 1995. [10] J. M. Kokernak, D. A. Torrey and M. Kaplan, “A switched reluctance

starter/ alternator for hybrid electric vehicles”, Power Electronics Proc.

(PCIM) Conference, pp. 74-80, 1999. [11] D. A. Torrey, “Variable reluctance generators in wind-energy systems,”

in Proc. IEEE Power Electronics Specialists Conf., 1993, pp. 561–567.

[12] L. Ribickis, E. Kamolins, N. Levin, and V. Pugachev, “One-Phase Reluctance Generators in Low-Power Wind Plants,” 12th European

Conference on Power Electronics and Applications (EPE2007), September 2007.

[13] R. Cardenas,W. F. Ray, and G.M. Asher, “Switched reluctance

generators for wind energy applications,” in Proc. IEEE Power Electronics Specialists Conf. (PESC95), 1995, pp. 559–564.

[14] Roberto Cardenas, Ruben Pena, Marcelo Perez, Jon Clare, Greg Asher,

and Patrick Wheeler, “Control of a switched reluctance generator for variable-speed wind energy applications,” IEEE Transactions on Energy

Conversion, vol. 20, no. 4, 2005.

[15] E. Echenique, J. Dixon, R. Cárdenas, and R. Peña, “Sensorless control for a switched reluctance wind generator, based on current slopes and

neural networks,” IEEE Transactions on Industrial Electronics, vol. 56,

no. 3, pp. 817-825, March 2009. [16] Hany M. Hasanien and S. M. Muyeen, “Design optimization of

controller parameters used in variable-speed wind energy conversion

system by genetic algorithms”, IEEE Transactions on Sustainable Energy, vol. 3, no. 2, pp. 200-208, April 2012.

[17] S.M. Muyeen, Hany M. Hasanien, and J. Tamura, “Reduction of

frequency fluctuation for wind farm connected power systems by an adaptive artificial neural network controlled energy capacitor system”,

IET Renewable Power Generation, vol. 6, no. 4, pp. 226-235, July 2012.

[18] Hany M. Hasanien, “A set-membership affine projection algorithm-based adaptive-controlled SMES units for wind farms output power

smoothing”, IEEE Transactions on Sustainable Energy, vol. 5, no. 4, pp.

1226-1233, October 2014. [19] Hany M. Hasanien, “Shuffled frog leaping algorithm-based static

synchronous compensator for transient stability improvement of a grid-

connected wind farm”, IET Renewable Power Generation, vol. 8, no. 6, pp. 722-730, August 2014.

[20] S. M. Muyeen, Hany M. Hasanien, and Ahmed Al-Durra, “Transient

stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES”, Energy

Conversion and Management, vol. 78, no. 2, pp. 412-420, February

2014. [21] Hany M. Hasanien, and S. M. Muyeen, “A Taguchi Approach for

Optimum Design of Proportional-Integral Controllers in Cascaded

Control Scheme”, IEEE Transactions on Power Systems,vol. 28, no. 2, pp. 1636-1644, May 2013.

[22] Hany M. Hasanien, and Essam. A. Al-Ammar, “Dynamic response

improvement of doubly fed induction generator based wind farm using fuzzy logic controller”, Journal of Electrical Engineering, Slovakia, vol.

63, no. 5, pp. 281-288, September 2012.

[23] O.Wasynczuk et.al. , “Dynamic behavior of a class of wind turbine generators during random wind fluctuations,” IEEE Transactions on

Power Apparatus and Systems, vol. l, PAS-100, no. 6, pp.2837-2854,

1981. [24] S. M. Muyeen, J. Tamura, and T. Murata, “Stability augmentation of a

grid-connected wind farm,” Springer-Verlag London, ISBN 978-1-

84800-315-6, October 2008. [25] Iqbal Husain, Arthur Radun, and John Nairus, “Fault analysis and

excitation requirements for switched reluctance generators,” IEEE

Transactions on Energy Conversion, vol. 17, no. 1, pp. 67-72, March 2002.

[26] Hany M. Hasanien, and S. M. Muyeen, “Speed control of grid-connected

switched reluctance generator driven by variable speed wind turbine using adaptive neural network controller”, Electric Power Systems

Research, vol. 84, no. 1, pp. 206-213, March 2012.

[27] Release 2008a, “MATLAB Toolbox,” The Math Works press, March 2008.

[28] Hany M. Hasanien, S. M. Muyeen, and Junji Tamura, “Speed control of

permanent magnet excitation transverse flux linear motor by using adaptive neuro-fuzzy controller”, Energy Conversion and Management,

vol. 51, issue 12, pp. 2762-2768, Dec. 2010.

[29] D. Driankov, H. Hellendoorn and M. Reinfrank, “An Introduction to Fuzzy Control”, Springer-Verlag, 1993.

[30] Hany M. Hasanien, “FPGA implementation of adaptive ANN controller

for speed regulation of permanent magnet stepper motor drives”, Energy Conversion and Management, vol. 52, issue 2, pp. 1252-1257, Feb.

2011.

[31] PSCAD/EMTDC Manual, Manitoba HVDC Research Center, 1994.

APPENDIX

The SRG specifications are illustrated in Table II.

TABLE II

The phase winding resistance 0.05 Ω

The DC supply voltage 240 V

The maximum phase current 200 A

The rated torque 152.79 N.m

The rated speed 3000 rpm

The rated power 48 KW

No. of motor phases 3

The rotor moment of inertia 0.05 Kg.m2

The friction coefficient 0.02 N.m.s