IEEE International Symposium on Industrial Electronics (ISlE 2009)Seoul Olympic Parktel, Seoul, Korea July 5-8, 2009

Improvement of Power System TransientStability by Two Control Methods: Study and

SimulationTelli Haman, Ahad Kazemi

Assuming entire loss of series and parallel braches is apercent of passing power, we can write the equationscorresponding to UPFC injection model as below:

operation in transient stability improvement [6]. This controlmethod improves the system trans ient stability by maxim izingand minimizing passing power flow .

Study and simulation of two neural network control methodbased on HX) learning method and discrete control areconsidered in this paper. The operations of these methods intransient stability improvement of single-machine and multi­machine systems are compared and considered.

(I)

(2)

(3)

(4)

Series Invert erShunt Inverter

SIN] = p;IN] + jQIN]

SIN] = pIN] +J'Q!N]) I I

p;IN] = ar2..V/ siny - (1 + a)rVitjsin(8ij + y)Xb

Fig. 1. The UPFC structure.

II. UNIFIEDPOWER FLOW CONTROLLER

UPFC is a FACT equipment which can control the passingpower flow by series voltage injection. The UPFC iscomposed of series and shunt source voltages which cancontrol three parameter of terminal voltage, line impedanceand phase angle at the same time. The UPFC structure isshown in Fig. I .

To model UPFC we can use different model s according tostudy type . A model which often is used in system transientstability relevant studies is called injection model. ConsiderFig. 2 which shows the electric equivalent diagram to describethis model.

Index Terms-» Transient Stability, UPI<'C, RBFNN, H,X)­learning, Discrete Control, PSS.

Abstract-- Using FACTS systems is one of the latest methodswhich has been used to improve transient stability of powersystems during recent years. These flexible systems have beenmajor determinant of transient stability improvement by powerswing damping. There are numerous suggested control methodsfor UPFC control. In this paper we will consider and comparetwo neural network control methods which are based on IL"learning method and discrete control method.

These control methods are implemented on both single­machine and multi-machine systems. The results of simulationshowed that RBFNN has better performance in domain andswing reduction by far and can be used as an appropriate optionin real time calculations.

Telli Haman is with the Department of Electrical Engineering, Iran.University of Science and Technology, Iran (e-mail:te lli.haman@ee.iust.ac.ir ).

Ahad Kazemi is with the Department of Electrical Engineering, Iran .University of Science and Technology, Iran (e-mail: kazemi @iust.ac.ir).

1. INTRODUCTION

U SING FACTS systems is one of the latest methods whichhas been used to improve transient stability of power

systems during recent years . These flexible systems have beenmajor determinant of transient stability improvement by powerswing damping.

There are several control methods recommended to controlUPFC which can be shown in [1]-[4] references. In the mostof recommended methods, injected serial voltage is dividedinto two components: perpendicular to line current and inphase with line current. Vertical component is more effectivein passing real power control whereas passing reactive powercontrol is mostly performed by in phase component. Neuralnetwork is one of the control methods which has beenconsidered during recent years. A neural network controlmethod which uses ax, learning method to update neuralnetwork parameters is suggested in reference [5]. In thisreference, the mentioned method is compared with oldermethods which use EKF filter (Extended Kalman Filter) andalso PID controller. The results represented in this referenceshows that HX) learning method operation is more effectivethan other methods .

Discrete control is a control method which is propoundedduring recent years and has shown a partly remarkable

978-1-4244-4349-9/09/$25.00 ©2009 IEEE1639

where rand yare independent variables and vary as below :

IN] 1 . ( )R}" =r-ViV}sm Bij+YXb

IN] _ 1 ( )Q}" -r-ViV}COS Bij+yXb

os r s rm ax , 0 s Y s ZIT

The UPFC injection model is shown in Fig. 3.

VI

Fig . 2. Electrical model of UPFC.

(5)

(6)

(7)

RBFNN is composed of three layers with fully differentfunctions (Fig. 4). The first layer is called input layer whichconsist of nodal resource posses connecting the network toRBFNN environment. The second layer (Hidden layer) has aduty to process an array of neural cells . However, there are agreat number of the calculating units often but reference [7]has shown using just one neural cell is sufficient for FACTSequipment real time control (Fig. 5). The last RBFNN layer isthe output layer.

Neural cell of controller's hidden layer designed for UPFCis called Radial Basis Function which imitates a Gaussfunction composed of two parameters : center parameter (u)and spread parameter (o), The response of this calculating unitto network(Y) input is:

0(y) = e -~IY-[l1 2 (8)

And network (f(y)) output is calculated as below :

(9)

H, learning method suggested in reference [7] is used forRBFNN parameter (X = [ao,avl.l,oy) matrix updating . Thedetails of the updating equation are as below:

(14)

(13)

(12)

(10)

( I I)Ks,k+1 = Pk+1IkHk+1(Hk+1Pk+1IkHk+1 + i)-1

- P - T T -1 [Hk ] PPk+1lk = klk-1-Pklk-dHk Hk]· Re,k n, klk-1

Re,k = Re,k+ [Z:] Pk1k-dHk HkJ

Rk = [~ -~JI]To globally optimized extended H, filter be in existence,

we must always have:

Bus,

Fig. 3. Thc UPFC injection model .

(15)

nmFig. 6 . The line between m and n bus .

Parameter rr is sensitive about initial value of relatedweights variations (X Parameters Matrix) but if the initialweights are left almost unchanged in a system, there is nolimit to choose Yr value and we can choose feasibly largeamount for it.

d~ d~

I~~Ix(k-n)

xtk-I )

x tk)

III. ROBUST n, CONTROL FOR UPFC

RBFNN (Radial Basis Function Neural Network) is analgorithm which has been used for nonlinear control of simplestructure systems during recent years. Overall structure of thiscontrol method is shown in Fig. 4.

Fig. 4. The neural network structure.

Fig. 5. Structure of Single-neuron RBFNN.

Input Layer Hidden Layer Output LayerAfter updating RBNFF X parameters, the controller output

value is defined according to previous X Parameters Matrix .We consider the injected voltages by UPFC serial branch

as in phase with and perpendicular to passing line current andname them Vcp and Vcr in sequence. As Vcr is perpendicular toline current, it can adjust the real passing line current. Also Vcpemerges from passing reactive line current (dQ = Qref - Q).

Disordering in passing real power is the most important factorof swing mode creation; therefore we can damp swings by

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At t=1.0 sec, a three-phase fault occurs in the marked pointin Fig. 8. At t=1.02 sec breaker cut is ordered and in t=1.05the fault is resolved and breaker is connected. The response ofsystem to this fault for different control strategies are shown inFig. 9 to Fig. 12.

The variations of power angle when simply PSS has beenused for swing damping are shown in Fig. 9. Also in Fig. 10you can see the variations of power angle for states usingdifferent control plans for UPFC. Comparing these figuresshows that variation of generator angle has remarkabledecrease and varied from 29° to 0.2°. In addition comparing toperformance of RBNFF control and discrete control showsthat the both have almost similar operation. However RBFNNoperates better slightly .

Then for SMIB system discrete control system isrepresented as[6]:

I. Control system should increase passing power as amassive disorder is recognized.

2. Control variables return to normal conditions valuesuntil dW

p = 0,~ < 0dt dt

3. When ~ is at its least quantity and do ~ -E UPFCdt dtcontrol variables act in a manner that decrease linepassing power as much as possible.

4. When do = 0 control variables return to normaldtconditions values.

5. Control variables act in a manner that line passingpower increases as much as possible until do reachesdtto its highest value and dO> E. Control process

tit:repeated from stage 2 regularly.

Control process of multi-machine system is similar tosingle-machine system .

Fig. 8. Single-machine infinite bus system.

V. SIMULATION RESULTS

In previous sections neural network control and discretecontrol were described. In this section we will present thesimulation of above control process in single-machine andmulti-machine.

All simulations are performed by PSCADIEMTDCsoftware. Designed control parameters of RBFNN in sequenceare:

K(} = 1.912, Kin = 0.2185. «, = 0.101, Yr = 108 (21)

A. Single-machine infinite bus

To experiment operation of designed controllers we firstlyconsider the transient stability in single-machine systemconnected to infinite bus. You can observe the mentionedsystem in Fig. 8.

(17)

(18)

(20)

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GeneticOptimization

P. - IVmlIVnl S'n(8 - 8 )m-n - Xm

- n L m n

IV. DISCRETE CONTROL FOR UPFC

Discrete control used for UPFC, improves the transientstability by line passing power maximizing and minimizing. Ifp:t is passing electric power of line after fault and Pm ismechanical power, the potential energy will obtained asbelow:

Fig. 7. The structure of Hs-learning controller.

P ref

If (Om - On) variation is a little value, then Sin(Om - On) ::::(Om - On) and IVm II Vn I "" IVm12

• Therefore:

Where !J.Om and !J.On are angle variations of m and n busesin relation with their initial values. Line real passing powercan be written as below:

Therefore measuring m bus voltage and real power passingfrom bus m to bus n leads to modulation signal generation . InRBF controller a neural cell has a duty to Vcr definition. Inputsignal of this controller proportions to difference value inKe{!J.Pmod(k) - !J.Pmod(k - 1)} inaccuracy and adapter block.Adapter block input which uses g-EHF filter is Kin!J.Pmod 'Control operation generated by RBFNN is a nonlinearcomposition of mentioned two inputs. In Fig. 7 you can seethe RBFNN controller structure for UPFC [5].To choose the optimized values for needed constants(Kin' Ke, Ke) genetic optimization is used as shown in Fig. 7. Ifthe network is composed of four generators, we can write thefitness function as below:

F = (:;sim(lwi - w31 + IW2 - w31 + IW4 - w31 + IW I -

w21). t. dt (19)

That are equal to ITAE (Integral Time Absolute Error)resulting from generators velocity . This error deduced fromswing modes. Integral time t sim is equal to simulation time ofsystem transient consideration.

adjusting it correctly. Then we can say that Vcr merges frompassing real power deviation (!J.P = Pret - P).If the line we wish to control its passing power is connected tobuses m and n (Fig. 6), we can use modulation signal resultingfrom angle difference between mentioned buses to dampsystem swings. So we can write:

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Power Angle

Fig. 9. Power angle (only PSS).

PowerAnglil

' . IUlFNN

I -.I

I •

One of the parameters used to evaluation of systemoperation intransient stability is the variation of velocity ingenerators. In Fig. II, velocity variations of network's uniquegenerator are shown for the state that just PSS is used. Also inFig. 12 variations of the generator's velocity are shown whenUPFC with different control structure is used. Comparingthese figures shows that UPFC has decreased the range of thegenerator's velocity. Additionally, control structure ofRBFNN is slightly better than discrete controller in operation.

B. Multi-ma chine power system

After consideration of performance of the designedcontrollers in single-machine system which is connected toinfinite bus, in this stage we will study the ability of RBFNNcontroller in compari son with discrete controller in a multi­machine system . The discussed system is chosen fromreference [5] and all its parameters are mentioned in reference[10] .

~ ~\ ~~

.." r ,...

.0 .100

T~

Fig. 10. Power angle (RBFNN and Discrete control strategies).

__--=,--- ~~!~.tor ...__=d

Fig. 11. Angular velocity of generator (only PSS).

Fig. 13. Multi-machine power system.

In Fig. 13 it can be seen this system along UPFC. Thissystem is composed of four generators and two areas whichare connected together by a 230 Km transfer line.

To study operation of the system in transient stabilityimprovement, we apply a three phase fault in middle point ofthe line between 8 and 9 buses. Duration of this fault is 100milliseconds. You can see the behavior of system at differentstates in Figures. 14-16.

Speecl01 gener. tor1. IUlFNN

' .~-I

1 •

Fig. 12. Angular velocity of generator (RBFNN and Discrete control Fig. 14. Local swing mode (RBFNN and Discrete control strategies).strategies).

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In the discussed multi-machine system composed of twoareas, local swing modes and inter-area modes exist. Localswing modes emerge from velocity difference of inter-areagenerators whereas inter-area swing modes emerge fromvelocity difference of the generator of an area with other area .Local swing modes for two discussed control plan are shownin Fig . 14.

-.I

second 1.000 1.0$0 1 100 1150 1.200 12$ll 1 30(1 \.3.50 1400 145(1 1.500 1.550 1600 1 650 1.700

Fig. 15. Inter-area swing mode (RBFNN and Discrete control strategies).

I~ -

Fig. 16. Inter-area swing mode (RBFNN and Discrete control strategies) .

Comparing operation of these control plans presents thatRBFNN performs better than discrete by far. Also inter-areaswing modes are shown in Fig. 15 and Fig. 16. By observingthese figures we can say that RBFNN has a better performancein inter-area swing modes damping.

VI. CONCLUSION

In this paper, an approach is suggested for transientstability improvement using UPFC. Therefore RBNFcontroller based on He", filter and discrete controller wasdesigned and operation of suggested process was consideredin single-machine and multi-machine systems. In section V.Athe results of simulation of suggested controllers in single­machine system connected to infinite bus was presented. Inthis section in addition of UPFC having the mentionedcontrollers, the situation in which simply PSS is used forswing damping was considered too. Survey of obtained resultsfrom single-machine systems connected to infinite bus whichwas presented in section V.A showed that the performance ofin transient stability is more effective when UPFC is used thanwhen simply PSS is used by far. Comparing Fig . 9 to Fig. 12approves the rectitude of this word. Also comparing thesefigures shows when UPFC with RBFNN controller is used,transient swings getting are damped sooner and with less

swing range. Obviously this RBFNN controller's high abilityin quicker response may cause its presence in real timefunctions.

In section V.B the mentioned process was implemented inmulti-machine system which was composed of two areas andfour machines. Comparing the obtained results impressesbetter performance of RBFNN in comparison with discretecontroller by a long way. RBFNN controller in addition todecreasing of swing range decreases the intensity of swingstoo .

VII. REFERENCES

[11. L. Gyugyi, c. D. Schauder, S. L. Torgerson , and A. Edris, "The unifiedpower flow controller: a new approach to power transmission control,"IEEE Trans. Power Del., vol. 10, no. 2, pp. 1088-1097, Apr. 1995.

[21. M. Noroozian, L. Angquist, M. Ghandari, and G. Anderson, "Improvingpower system dynamics by series-connected FACTS devices ," IEEETrans. Power Del., vol. 12, no. 4, pp. 1635-1641 , Oct. 1997.

[31. M. Noroozian and G. Anderson, "Damping of power system bycontrollable components," IEEE Trans. Power Del., vol. 9, no. 4, pp.2046-2054, Oct. 1994.

(4). K. R. Padiyar and A. M. Kulkarni, "Control design and simulation ofunified power flow controller," IEEE Trans. Power Del., vol. 13, no. 4,pp. 1348-1354, Oct. 1998.

(5). Sukumar Mishra, "Neural-Network-Based Adaptive UPFC forImproving Transient Stability Performance of Power System", IEEETRANSACTION ON NEURAL NETWORKS, VOL. 17, NO.2,MARCH 2006, pp.461-470.

(6). S.Krishna and K.R.Padiar , "Discrete control of unified power flowcontroller for stability improvement", Electric Power System Research75(2005), pp.l78-189.

[7). P. K. Dash, S. Mishra, and G. Panda, "A radial basis function neuralnetwork controller for UPFC," IEEE Trans. Power Syst., vol. 15, no. 4,pp.1293-1299,Nov.2000.

(8). D. E. Goldberg, "Genetic Algorithm in Search, Optimization, andMachine Learning. Reading", MA: Addison-Wesley, 1989.

(9). K.R. Padiyar, S. Krishna, "On-line detection of loss of synchronismusing locally measurable quantities" , IEEE Transmission andDistribution Conference and Exposition, Atlanta, October, 2001.

[10).1'. Kundur, "Power System Stability and Control", McGraw-Hill, 1994.

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