Electrical Power and Energy Systems 31 (2009) 402–408

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Electrical Power and Energy Systems

journal homepage: www.elsevier .com/locate / i jepes

Enhancement of transient stability by fuzzy logic-controlled SMES consideringcommunication delay

Mohd Hasan Ali a,*, Minwon Park b, In-Keun Yu b, Toshiaki Murata c, Junji Tamura c, Bin Wu a

a Dept. of Electrical and Computer Engineering, Ryerson University, 245 Church Street, Toronto, Ontario, Canada M5B 1Z2b Dept. of Electrical Engineering, Changwon National University, Changwon, Gyeongnam 641-773, South Koreac Dept. of Electrical and Electronic Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan

a r t i c l e i n f o a b s t r a c t

Article history:Received 5 March 2007Received in revised form 13 March 2009Accepted 20 March 2009

Keywords:Superconducting magnetic energy storage(SMES)Power system transient stabilityCommunication delay

0142-0615/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.ijepes.2009.03.022

* Corresponding author. Tel.: +1 416 979 5000x610E-mail addresses: hasan.ani@hotmail.com, mali@ee

This paper presents a fuzzy logic-controlled superconducting magnetic energy storage (SMES) for theenhancement of transient stability in a multi-machine power system. The control scheme of SMES isbased on a pulse width modulation (PWM) voltage source converter (VSC) and a two-quadrant DC–DCchopper using gate-turn-off (GTO) thyristor. Total kinetic energy deviation (TKED) of the synchronousgenerators is used as the fuzzy input for SMES control. Communication delays introduced in online cal-culation of the TKED are considered for the actual analysis of transient stability. Global positioning sys-tem (GPS) is proposed for the practical implementation of the calculation of the TKED. Simulation resultsof balanced fault at different points in a multi-machine power system show that the proposed fuzzylogic-controlled SMES is an effective device for transient stability enhancement of multi-machine powersystem. Moreover, the transient stability performance is effected by the communication delay.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Superconducting magnetic energy storage (SMES) is a largesuperconducting coil capable of storing electric energy in the mag-netic field generated by DC current flowing through it [1]. The realpower as well as the reactive power can be absorbed by or releasedfrom the SMES coil according to system power requirements. Sincethe successful commissioning test of the BPA 30 MJ unit [2], SMESsystems have received much attention in power system applica-tions, such as, diurnal load demand leveling, frequency control,automatic generation control, uninterruptible power supplies, etc.

‘A number of articles [3–7] have been reported demonstrating theuse of SMES unit for power system transient stability enhancement.However, in all of these works, SMES is controlled through conven-tional controllers. The effectiveness of SMES on power system stabil-ization depends on its proper control strategy. Therefore, althoughthe strategies [3–7] for SMES control have been proposed in the lit-erature, the real problem has been and still is the determination ofthe best or optimal switching strategies. So, continuous attemptsto explore new and effective control options are ongoing. Fuzzy logicis a powerful problem-solving methodology with a myriad of appli-cations in embedded control and information processing. Fuzzy logicprovides a remarkably simple way to draw definite conclusions fromvague, ambiguous or imprecise information. In a sense, fuzzy logicresembles human decision making with its ability to work from

ll rights reserved.

9; fax: +1 416 979 5280..ryerson.ca (M.H. Ali).

approximate data and find precise solutions. The control methodof modeling human language has many advantages, such as simplecalculation, high robustness, lack of a need to find the transfer func-tion of the system, suitability for nonlinear systems, etc. [8].

Considering these viewpoints, this paper presents a fuzzy logic-controlled SMES for the enhancement of transient stability in amulti-machine power system. Exploiting the concept of fuzzy logiccontrol, we reported a work [9] on the transient stability enhance-ment of a single machine power system by SMES. In [9], it wasdemonstrated that the performance of fuzzy logic-controlled SMESis better than that of proportional-integral (PI) controlled SMES. Itis important to note here that unlike our previous result [9] inwhich just thyristor based converter was used for SMES control,in this work a pulse width modulation (PWM) voltage source con-verter (VSC) and a two-quadrant DC–DC chopper using gate-turn-off (GTO) thyristor are used for SMES control. Therefore, the SMEScan be operated to provide independent control of real and reactivepower [10–14]. Charge and discharge of SMES are determined bythe chopper duty cycle, which is controlled by the fuzzy logic. Totalkinetic energy deviation (TKED) of the synchronous generators isused as the input to the fuzzy logic controller.

For the practical implementation of our proposed method, realtime calculation of the TKED is required. Global positioning system(GPS) [15–20] is proposed for the online calculation of the TKED.However, communication delays are introduced in online calcula-tion of the TKED, which may affect the control system. Conse-quently, the transient stability of the system would be affected.So, the communication delay phenomenon associated with the

Efd Vt

4.0

-4.0

1+0.5S

25

Efdo Vto

Fig. 2. AVR model.

m P 1+2.0S

mo

1.05

0.0

Po

20

Fig. 3. GOV model.

M.H. Ali et al. / Electrical Power and Energy Systems 31 (2009) 402–408 403

online calculation of the TKED should be considered for the actualanalysis of transient stability. The most important and the novelfeature of this work is that the transient stability analysis is carriedout considering such communication delays.

The organization of this paper is as follows: Section 2 describesthe model system for the proposed study. Section 3 describes thecontrol scheme of SMES. Section 4 explains the online calculationmethod of TKED by using GPS. Section 5 describes the simulationresults. Finally, Section 6 provides some conclusions regarding thiswork.

2. Description of model system

For the simulation of transient stability, the 9-bus power systemmodel [21] as shown in Fig. 1 has been used. The system model con-sists of two synchronous generators (G1 and G2) and an infinite busconnected to one another through transformers and double circuittransmission lines. In the figure, the double circuit transmissionline parameters are numerically shown in the forms R + jX (jB/2),where R, X, and B represent resistance, reactance, and susceptance,respectively, per phase with two lines. Various parameters of thegenerators [21] used in this work are shown in Table 1, while theautomatic voltage regulator (AVR) and governor (GOV) control sys-tem models [21] shown in Figs. 2 and 3, respectively, are consideredto make the system more sophisticated. In Fig. 2, Vt indicates gener-

G1

j0.0625

G2

(P/Q=1.9/0.1) (P/V=1.2/1.0

0.0085+j0.072 (j0.0745)

0.0119+j0.1008 (j0.1045)

(P/Q=1.0/0.35)

(P/Q=1.25/0.5) (P/Q=0.9/0.3) 0.010+j0.085 (j0.088) 0.017+j0.092

(j0.079)

0.032+j0.16 (j0.153

0.039+j0.170 (j0.179)

j0.0586

j0.0576

Infinite bus

Tr.1 Tr.2

Fault

50Hz, 100MVA BASE

F1

Tr.3

1.04 0.0∠

Load A Load B

Load C

1

2 3

4

65

7

8

9

CB

F2

F3

º

Fig. 1. 9-Bus power system model.

Table 1Generator parameters.

G1 G2

MVA 200 130

ra (pu) 0.003 0.004

xa (pu) 0.102 0.078

Xd (pu) 1.651 1.220

Xq (pu) 1.590 1.160

X0d (pu) 0.232 0.174

X0q (pu) 0.380 0.250

X00d (pu) 0.171 0.134

X00q (pu) 0.171 0.134

T 0do (s) 5.900 8.970

T 0qo (s) 0.535 1.500

T 00do (s) 0.033 0.033

T 00qo (s) 0.078 0.141

H (s) 9.000 6.000

ator terminal voltage, Vto reference of terminal voltage, Efdo refer-ence of field voltage, and Efd field voltage; while in Fig. 3, xm

indicates generator rotor speed, xmo speed reference, Po referenceof input mechanical power, and P input mechanical power. TheAVR controls the excitation voltage of the synchronous machineand attempts to keep constant terminal voltage. The GOV controlsmechanical input power of the synchronous machine and attemptsto keep constant angular velocity. Both AVR and GOV control mod-els have first-order delay and limiter blocks.

3. Control scheme of SMES

3.1. Brief overview of SMES system

A SMES device is a DC current device that stores energy in themagnetic field. The DC current flowing through a superconductingwire in a large magnet creates the magnetic field. The large super-conducting coil is contained in a cryostat or dewar consisting of avacuum vessel and a liquid vessel that cools the coil. A cryogenicsystem is also used to keep the temperature well below the criticaltemperature of the superconductor. A bypass switch is used to re-duce energy losses when the coil is on standby.

In order to effectively control the power balance of the synchro-nous generators during dynamic period, two SMES units are usedat the terminal busses of generators G1 and G2 of the power sys-tem model. Fig. 4 shows the basic configuration [14] of one ofthe proposed SMES units, which consists of a Wye-Delta 20 kV/3 kV transformer, a 6-pulse PWM rectifier/inverter using GTO thy-ristor, a two-quadrant DC–DC chopper using GTO, and a supercon-ducting coil or inductor. The PWM converter and the DC–DCchopper are linked by a DC link capacitor of 5000 lF.

For a SMES system, the inductively stored energy (E in joule)and the rated power (P in watt) are commonly the given specifica-tions for SMES devices, and can be expressed as follows:

E ¼ 12

LsmI2sm

P ¼ dEdt¼ LsmIsm

dIsm

dt¼ VsmIsm

ð1Þ

where Lsm is the inductance of the coil, Ism is the DC current flowingthrough the coil, and Vsm is the voltage across the coil. The ratings ofthe proposed SMES units are shown in Table 2.

3.2. PWM voltage source converter

The PWM voltage source converter (VSC) provides a power elec-tronic interface between AC power system and superconducting

Ism (DC current)

Vcap

DC link capacitor

SMES coil

Three-phase AC

(from generator

terminal bus)

Y/transformer (20/3KV)

Voltage source converter using GTO

DC-DC chopper using GTO

Bypass switch

1 3 5

264

Fig. 4. Basic configuration of SMES system.

Table 2Ratings of SMES.

SMES unit 1 SMES unit 2

Power (MW) 200 130Energy (MW H) 0.04 0.03Coil inductance (H) 0.16 0.18

R jX I

Vconv∠α °Vac ∠ 0 ° P,Q

VSC

.

Fig. 5. VSC single phase equivalent circuit.

Kp=1.2

Ti=0.2

Δ

KP

KP/Tis +

∑

Δ

Kp=1.2

Ti=0.2 ∑

convR

convI

V

V1tan −=α

Fig. 6. Block diagrams of PI controller to determine a.

404 M.H. Ali et al. / Electrical Power and Energy Systems 31 (2009) 402–408

coil. In the PWM generator, the sinusoidal reference signal is phasemodulated by means of the phase angle, a, of the VSC output ACvoltage. Let us consider the simplified VSC single phase equivalentcircuit shown in Fig. 5, where Vac and Vconv are the rms values of theAC system line voltage and VSC output AC voltage, respectively, Rand X are the converter transformer resistance and leakage reac-tance, respectively.

With positive reference directions of real power, P, and reactivepower, Q, as shown in the figure, we have the following equations,where VconvR and VconvI indicate real and imaginary components ofthe VSC output AC voltage, respectively, _I is the phasor quantity ofthe current flowing from AC system side to the converter side, andIR and II are the real and imaginary component of _I, respectively

_I ¼ Vac � ðVconvR � jVconvIÞRþ jX

ð2Þ

IR ¼1

R2 þ X2 ½RðVac � VconvRÞ � XVconvI� ð3Þ

II ¼�1

R2 þ X2 ½XðVac � VconvR þ RVconvIÞ� ð4Þ

P ¼ ReðVac_I�Þ ¼ VacIR ð5Þ

Q ¼ ImðVac_I�Þ ¼ VacII ð6Þ

From (5) and (6), it can be shown that

P / IR and Q / II

Again, if R is very small, then from (3) and (4) it can be shownthat

IR / VconvI and II / VconvR

Therefore, it can be said that

P / VconvI and Q / VconvR

Based on this concept, the phase angle, a, is determined fromthe outputs of the PI controllers as shown in Fig. 6, where DVcap

and DQac indicate the capacitor voltage deviation and reactivepower deviation of the AC system side, respectively. The PI control-ler parameters are determined by trial and error in order to obtainthe best system performance. In this work, the amplitude modula-tion index of the sinusoidal reference signal is chosen 1.0. Themodulated sinusoidal reference signal is compared with the trian-gular carrier signal in order to generate the gate signals for theGTOs. The frequency of the triangular carrier signal is chosen450 Hz. The DC voltage across the capacitor is 4000 V, which iskept constant throughout by the 6-pulse PWM converter.

3.3. Two-quadrant DC–DC chopper

The superconducting coil is charged or discharged by adjustingthe average (i.e., DC) voltage across the coil to be positive or nega-tive values by means of the DC–DC chopper duty cycle, D, con-trolled by the fuzzy logic. When the duty cycle is larger than 0.5or less than 0.5, the coil is either charging or discharging, respec-tively. When the unit is on standby, the coil current is kept con-stant, independent of the storage level, by adjusting the chopperduty cycle to 50%, resulting in the net voltage across the supercon-ducting winding to be zero. In order to generate the gate signals forthe GTOs of the chopper, the PWM reference signal is comparedwith the saw tooth carrier signal. The frequency of the saw toothcarrier signal for the chopper is chosen 1000 Hz.

M.H. Ali et al. / Electrical Power and Energy Systems 31 (2009) 402–408 405

3.4. Design of fuzzy logic controller

A fuzzy logic, unlike the crispy logic in Boolean theory that usesonly two logic levels (0–1), is a branch of logic that admits infinitelogic levels (from 0 to 1), to solve a problem that has uncertaintiesor imprecise situations. Again, a fuzzy control is a process controlthat is based on fuzzy logic and is normally characterized by ‘‘IF–THEN” rules. The design of the proposed fuzzy logic controller isdescribed in the following.

3.4.1. FuzzificationFor the design of the proposed fuzzy logic controller, total ki-

netic energy deviation, TKED, of the synchronous generators, andchopper duty cycle, D, are selected as the input and output, respec-tively. Triangular membership functions for TKED are shown inFig. 7 in which the linguistic variables N, Z, and P stand for nega-tive, zero, and positive, respectively. The equation of the triangularmembership function used to determine the grade of membershipvalues is as follows [8]:

lAi ðTKEDÞ ¼ 1=bðb� 2jTKED� ajÞ ð7Þ

where lAi(TKED) is the value of grade of membership, ‘b’ is thewidth, ‘a’ is the coordinate of the point at which the grade of mem-bership is 1 and ‘TKED’ is the value of the input variable.

3.4.2. Fuzzy rule baseThe specific feature of the proposed fuzzy controller is its very

simple design having only one input variable and one output var-iable. The use of single input and single output variable makes thefuzzy controller very straightforward [9,21,22]. The control rules ofthe proposed controller are determined from the viewpoint ofpractical system operation and by trial and error and are shownin Table 3.

3.4.3. InferenceFor the inference mechanism of the fuzzy controller design,

Mamdani’s method [8] is used. According to Mamdani, the degreeof conformity, Wi, of each fuzzy rule is as follows:

Wi ¼ lAiðTKEDÞ ð8Þ

where lAi(TKED) is the value of grade of membership and i is rulenumber.

3.4.4. DefuzzificationThe center-of-area method is the most well-known and rather

simple defuzzification method [8] which is implemented to deter-mine the output crispy value (i.e., the chopper duty cycle, D). Thisis given by the following expression:

-0.2 -0.1 0.0 0.1 0.2

Fig. 7. Membership functions of TKED (pu).

Table 3Fuzzy rule table.

TKED (pu) D (duty cycle)

N SmallZ MediumP Big

D ¼X

WiCi

XWi

.ð9Þ

where Ci is the value of D expressed in terms of linguistic variablesin the fuzzy rule table.

4. Online calculation of TKED by GPS and associatedcommunication delay

In this work, the total kinetic energy deviation, TKED, of thesynchronous generators is used as the fuzzy controller input forDC–DC chopper. The difference between the total kinetic energy(Wtotal) of the generators at transient state and that at steady stateis defined as total kinetic energy deviation, TKED, i.e., TKED =(Wtotal at transient state) � (Wtotal at steady state). In order to cal-culate TKED, Wtotal is needed, which can be determined easily byknowing the rotor speed of each generator and is given by:

Wtotal ¼XN

i¼1

Wi ðJÞ ð10Þ

where

Wi ¼12

Jix2mi ðJÞ ð11Þ

denotes kinetic energy in joule for a generator, Wtotal total kineticenergy in joule, i is generator number and N total number of gener-ators. Again, in (11) Ji = (H �MVA rating)/{5.48 � 10�9 � (NS)2} de-notes moment of inertia in kg m2 where NS and H aresynchronous angular speed in rpm and inertia constant, respec-tively, and xmi = 2 � p � (N/60) rotor angular velocity in mechani-cal rad/sec where N is rotor speed in rpm.

The online calculation of the TKED using the speed signal ofeach generator, and then again using the signal of the TKED asthe input to each fuzzy controller, can be accomplished by GPS[15–20] which provides time synchronization of signals. GPS is aUS Department of Defense radio-navigation system consisting of24 satellites placed into orbit and arrayed to provide at least foursatellites visibility at all times. Each satellite transmits a navigationsignal from which a receiver can decode time synchronized towithin 0.2 ls of coordinated universal time (UTC), the world stan-dard. The inherent availability, redundancy, reliability, and accu-racy make it a system well suited for synchronized phasormeasurement systems [15]. It has recently been recognized thatsynchronized measurement of power system quantities is feasibleusing the GPS, since GPS can easily and precisely provide a timesignal, with a 1 ls accuracy, at any location on the power network[16].

Fig. 8 shows a simplified functional block diagram where theGPS receiver collects the digitalized speed equivalent signals ofthe generators, and synchronizes the signals in a common timingreference. The synchronized signals are then sent to a central con-trol office where Wtotal as well as TKED is calculated. Data output,i.e., the signal of TKED is then sent to each fuzzy controller input.In this case, signals may be transmitted and received throughmicrowave or optical fibre.

However, there exist various time delays (lags) in power systemmeasurement [23]. During online calculation of TKED, time delaysare introduced mainly due to signal transmission through opticalfibre or microwave, A/D conversion, calculation of Wtotal as wellas TKED, and time synchronization of signals by GPS. The commu-nication delays may affect the control logic, and consequently thetransient stability of the system may be affected. So, such commu-nication delays should be considered for the actual analysis of tran-sient stability.

Typically 150–200 ms communication delays are considered todesign some actual transient stability control systems [24,25]. In

Speed equivalent signals from the generators

GPS Receiver

Time & Sync

Data output (TKED) to fuzzy controller

A/D converter

Digitalized Speed signals

Central control office calculation of TKED

Fig. 8. GPS functional block diagram.

Table 4Values of Wc without considering communication delay.

Fault point Values of Wc (s)

With fuzzy controlled SMES Without SMES

F1 2.34 4.99F2 2.24 3.55F3 2.27 3.63

(a) Without SMES

0 2 4 6 8 1 010

20

30

40

50

60

70

80

90

100

110

Loa

d an

gle

[deg

]Time [sec]

G1

G2

100

110

406 M.H. Ali et al. / Electrical Power and Energy Systems 31 (2009) 402–408

this work, extensive simulations are carried out considering vari-ous values of communication delays. Some of the simulation casescorresponding to communication delays from 150 to 200 ms aredescribed in Section 5.2.

(b) With SMES

0 2 4 6 8 1 010

20

30

40

50

60

70

80

90

Loa

d an

gle

[deg

]

Time [sec]

G1

G2

Fig. 9. Load angle responses for 3LG fault at point F1 without consideringcommunication delay.

5. Simulation results and discussion

The effectiveness of the proposed method is demonstratedthrough simulations by using electro-magnetic transients program(EMTP), a special transient simulation program which can predictvariables of interest in electric power networks as functions oftime, typically following some disturbances such as the switchingof a circuit breaker, or a fault [26]. Simulations have been carriedout considering balanced (three-phase-to-ground: 3LG) fault atdifferent points on the transmission lines of the power system.Time step and simulation time have been chosen as 50.0 lsecand 10.0 sec, respectively.

5.1. Transient stability enhancement without consideringcommunication delay

In order to understand the effect of communication delaysintroduced in online calculation of TKED on transient stability, firstsimulations were carried out without considering communicationdelays. For the evaluation of transient stability we have used thestability index, Wc [22], given by

Wc ðsÞ ¼Z T

0

ddt

Wtotal

��������dt�

system base power ð12Þ

where T is the simulation time selected to 10.0 sec, Wtotal is the totalkinetic energy, which is already explained in (10). The lower the va-lue of Wc, the better the system’s performance.

Table 4 shows the values of Wc for 3LG fault at points F1, F2, andF3 as shown in Fig. 1 without considering communication delay. Itis clearly seen that the fuzzy controlled SMES is effective inimproving the transient stability for all fault points.

Fig. 9 shows the load angle responses for 3LG fault at point F1without considering communication delay. It is seen from the loadangle responses that the system is transiently stable when SMES isused.

5.2. Effect of communication delay on transient stability

In this work, for the transient stability analysis, extensive sim-ulations are carried out considering various values of communica-tion delays. Table 5 shows the values of Wc with SMEScorresponding to various values of communication delays in caseof 3LG fault at points F1, F2, and F3. It is seen that the fuzzy con-trolled SMES is effective in improving the transient stability for3LG fault at different points. It is also observed that the values ofWc corresponding to various communication delays are differentfor different fault points. Moreover, compared with the values ofWc with fuzzy controlled SMES as shown in Table 4, the values ofWc with SMES as shown in Table 5 are higher. This fact indicatesthat the communication delay associated with the online calcula-tion of the fuzzy controller input for SMES has an effect on thetransient stability.

Fig. 10 shows the load angle responses with SMES for 3LG fault atpoint F1 corresponding to communication delays of 150 and200 ms. It is seen from these responses that the system is transientlystable when SMES is used. It is also observed that the load angleresponses are somewhat different corresponding to different com-

0 2 4 6 8 1 010

20

30

40

50

60

70

80

90

100

110

Loa

d an

gle

[deg

]

Time [sec]

G1

G2

0 2 4 6 8 1010

20

30

40

50

60

70

80

90

100

110

Loa

d an

gle

[deg

]

Time [sec]

G1

G2

(a) for a communication delay of 150 msec.

(b) for a communication delay of 200 msec

Fig. 10. Load angle responses with SMES for 3LG fault at point F1 consideringcommunication delay.

Table 5Values of Wc considering communication delay.

Fault point Communication delay (ms) Values of Wc (s) with SMES

F1 150 2.70160 2.79170 2.81180 2.88190 2.95200 2.99

F2 150 2.28160 2.33170 2.47180 2.49190 2.52200 2.55

F3 150 2.33160 2.37170 2.41180 2.46190 2.53200 2.56

M.H. Ali et al. / Electrical Power and Energy Systems 31 (2009) 402–408 407

munication delays. Moreover, there is a difference among the loadangle responses with SMES as shown in Fig. 9b, and the load angleresponses with SMES as shown in Fig. 10. This fact also indicatesthat the communication delay associated with the online calcula-tion of the fuzzy controller input has an effect on the transientstability.

One important point to note here is that the fuzzy controllerparameters were the same throughout the simulations for all faultlocations, and communication delays. This fact substantiates therobustness of the proposed fuzzy logic controller for SMES.

5.3. Cost-effectiveness of SMES

The SMES is an expensive device. However, due to its salientproperties such as very fast response, high efficiency, capabilityof real power and reactive power control, etc., SMES system is get-ting increasing interest in the field of power systems. It is hopedthat its potential advantages and environmental benefits will makeSMES units a viable alternative for energy storage and manage-ment devices in the future [27,28]. And although at present thecost of a SMES unit appears somewhat high, continued researchand development is likely to bring the price down and make thetechnology appear even more attractive.

6. Conclusion

This paper presents a fuzzy logic-controlled SMES for theenhancement of transient stability in a multi-machine power sys-tem. Communication delays introduced in online calculation of theTKED are considered for the actual analysis of transient stability.From the simulation results of balanced fault at different pointsin the system, the following conclusions can be drawn.

(a) The fuzzy logic-controlled SMES is effective in improving thetransient stability of a multi-machine power system.

(b) The communication delay associated with the online calcu-lation of the fuzzy controller input for SMES has an effecton the transient stability of a multi-machine power system.

Future research will focus on the investigation of the controlalgorithms to minimize the negative effect of communication de-lays on the transient stability. Fuzzy interval would be used inour future work. Moreover, an adaptive fuzzy logic controller forSMES would be designed by the application of artificial neural net-work or genetic algorithm technique. We hope to publish our re-search results in the near future.

Acknowledgement

This work was supported by the Brain Korea 21 Project Corps atChangwon National University, South Korea.

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