new methods for computing power system dynamic

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324 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMSI: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 47, NO. 3, MARCH 2000

New Methods for Computing Power System DynamicResponse for Real-Time Transient Stability Prediction

Chih-Wen Liu , Member, IEEE and James S. Thorp , Fellow, IEEE

Abstract Using new systems capable of making synchronizedphasor measurements, the real-time stability assessment of atransient event in power systems has become an important areaof investigation. Using these phasor measurements as inputconditions for computing a relatively good, simplified dynamicmodel can yield accurate and real-time transient stability predic-tion in a central location equipped with high-speed computers.In an effort to reduce the computing time for integrating thedifferential/aAlgebraic equation (DAE) model of postfault powersystem dynamics for prediction use, this paper presents a fasterImplicitly Decoupled PQ Integration technique. Two piecewisedynamic equivalents are also proposed, i.e., piecewise constantcurrent load equivalent and piecewise constant transfer admit-

tance equivalent. These equivalents can eliminate the algebraicequations by approximating the load flow solution piecewiselysuch that only internal generator buses are preserved, whileapproximately retaining the characteristics of the nonlinear loads.The proposed techniques have been tested on two sample powersystems with promising simulation results.

Index Terms Decoupled integration, dynamic response, piece-wise equivalent, phasor measurements, transient stability.

I. INTRODUCTION

W ITH the advent of phasor measurement units (PMU's)[1][4] capable of tracking the dynamics of an electricpower system, together with modern telecommunication abil-ities, utilities are becoming able to respond intelligently to anactual event in progress. By the synchronization of samplingwith microprocessor based systems, phasor calculations can beplaced on a common time scale [ 5]. Commercially availablesystems based on Global Positioning System (GPS) satellitetime transmissions can provide synchronization to 1 s accu-racy, which means that a relative phase angle can be measuredto a precision of 0.02 electrical degrees. Utility experience in-dicates that communication systems can transmit these time-tagged phasor measurements to a central location every five cy-cles [6]. The magnitudes and angles of these phasors indicatethe state of a power system, and are used in state estimation andtransient stability analysis [ 7].

The power system itself provides us with the system trajec-tory up to the current time. The time track of state variablesand several of their derivatives are available in an observationwindow (Fig. 1). Thus, it seems possible that the outcome for a

Manuscript received April 7, 1997; revised September 2, 1998 and June 13,1999. This paper was recommended by Associate Editor A. Ioinovici.

C.-W. Liu is with the Department of Electrical Engineering, National TaiwanUniversity, Taipei, Taiwan (e-mail: [email protected]).

J. S. Thorp is with the School of Electrical Engineering, Cornell University,Ithaca, NY 14850 USA (e-mail: [email protected]).

Publisher Item Identifier S 1057-7122(00)02310-2.

Fig. 1. Prediction of power system stability for adaptive protection.

future time interval of an evolving swing can be calculated withrelatively accurate simplified dynamic models. If the outcomeof a developing swing can be predicted in real time, it is pos-sible to initiate appropriate protection and control actions, suchas out-of-step blocking and tripping, or fast-valve control of tur-bines.

One such real-time control strategy is already being imple-mented at the FloridaGeorgia interface [ 8]. An important fea-ture of the FloridaGeorgia situation is that interarea oscilla-tions between the two regions can always be modeled as a sim-plified two-machine equivalent system. When interarea oscil-lations occur, phasor measurements are taken within Floridaand Georgia in order to develop the corresponding state of thetwo-machine equivalent. Future stability is then determined byapplying the equal area criterion. This method of prediction isused for adaptive out-of-step relaying at the FloridaGeorgia in-terface.

Many existing transient stability assessment techniques,although simple in off-line application, are too complicatedfor real-time prediction use. However, real-time monitoringobviates the need for some of these techniques since the systemitself is actually solving the differential/algebraic equations(DAEs). What is required is a computationally efficient wayof processing real-time measurements to determine whetheran evolving event will ultimately be stable or unstable. Theproblem of real-time stability prediction for a simplifiedmodel can be answered with powerful workstations in controlcenters, which enable one to integrate the system trajectory inreal time. Given a simplified model and an initial condition(observed phasor measurements), a workstation takes just a

10577122/00$10.00 2000 IEEE

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LIU AND THORP: NEW METHODS FOR COMPUTING POWER SYSTEM DYNAMIC RESPONSE FOR REAL-TIME TRANSIENT STABILITY PREDICTION 325

few hundredths of a second to compute a system trajectory onesecond into the future.

In an effort to reduce the computing time for integratinga DAE description of a postfault dynamic model, this paperpresents an efficient implicitly decoupled PQ integration(decoupled integration) technique. While DAE description isaccurate for an adopted model, it becomes complex as the

size of a study system increases. Therefore, two piecewisedynamic equivalents are proposed, i.e., the piecewise constantcurrent load equivalent (current load equivalent) and piecewiseconstant transfer admittance equivalent (admittance equiva-lent). Together, these can eliminate the algebraic equationsby approximating the load flow solution piecewisely so thatonly internal generator buses are preserved, while retaining theapproximate characteristics of nonlinear loads.

II. REAL-TIME PREDICTION PROBLEM AND DYNAMIC MODEL

Our research addresses the question of accomplishing sta-bility prediction when the system does not always reduce to a

previously known two-machine equivalent. Possible methods of approaching this problem which we have investigated fall intotwo broad categories.

1) Infer a small-size (e.g., two-, three-, or four-machine)equivalent from the postfault phasor measurements,which can model the particular oscillation mode of thefault in progress. Solve the model forward in time inorder to predict future behavior.

2) Use an appropriately simplified postfault dynamic modelof the system (e.g., the 39 bus model for New England)which adequately covers the many modes of oscillationinitiated by different events. Solve the model faster thanreal time if computational resources permit, or else traina pattern recognition tool (e.g., decision trees and neuralnetworks) off-line in order to associate in real-time thepostfault phasor measurements with the outcome of fu-ture behavior.

Previously, in [ 9][11] short-term prediction algorithms for theabove two strategies were proposed. In this paper, we proposenew methods to solve the dynamic model faster than real timeto enhance the second strategy.

An appropriately simplified postfault dynamic model playsa critical role in real-time stability prediction problem. Adetailed model, although more accurate for prediction of atransient event over a longer window may involve so much

computation such that it cannot meet real-time demand. On theother hand, an overly simplified model, such as the classicalmodel of a generator with a constant impedance load model,has been observed to give overly optimistic stability results[12]. In a real-time stability prediction problem, a model whichgives more conservative results is preferable to a model whichgives more optimistic results. This is because failure to executenecessary special protection schemes in real time can provequite costly [ 13]. It seems that the classical model of generatorwith static composite constant impedance, constant currentand constant power (referred to as ZIP) load model offersan effective compromise between accuracy and simplicityrequired for real-time applications [ 12]. Therefore, in our study,

we adopt the classical model of generator with ZIP load modelto describe the dynamics of a postfault power system. We firstderive the DAE description for the adopted model, and thenderive piecewise dynamic equivalents.

A. Differential/Algebraic Equation Description

Suppose there are generators and buses connectedby transmission lines and transformers, with buseshaving loads and no generations. It is convenient to introducefictitious buses representing the internal generation voltages.These fictitious buses are connected to the generator buses viareactances accounting for transient reactances and connectinglines. Thus the total number of buses in the augmented systemis buses. We order the buses as follows:

generator internal busesgenerator terminal busesload buses

A quasi-static network/load representation is assumed. Letthe complex voltage at th bus be the phasor , where

is the bus phase angle with respect to a synchronously ro-tating reference frame. The th generator frequency deviation isdenoted as . The network is represented by the node(bus) admittance matrix .

1) Generator Dynamic Equations: We assume that the gen-erators are modeled by constant internal voltage sources behindtransient reactance. The angle of this voltage is determined bythe swing equation

(1)

(2)

wheregenerator inertial constantgenerator damping coefficientmechanical power input

2) Transmission System Equations: Fora lossy transmissionsystem the power flowing through the transmission lines con-nected to the th bus can be written as

(3)

(4)3) Load Equations: Here, we introduce the polynomial

function of the static ZIP load model at load bus . A static

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ZIP model is a composite load which consists of constantimpedance, constant current and constant power loads, allin parallel. Thus, the load model is nonlinear and the powerbalance equations for the system loads can be written as

(5)

(6)

Here , and are coefficients that express the propor-tion of various loads combined together. is the voltage mag-nitude of load bud . Equations (1)(6) constitute the systemmodel, which is a set of DAE's of the form

(7)

(8)

(9)

(10)

Define the following vectors and matrices:

We can rewrite (7)(10) in more compact vector forms(11)

(12)

(13)

(14)

Remark: Define the Jacobin, , matrix as follows:

Then, by the implicit function theorem, if the Jacobin isnonsingular, locally there exists a mapping, , such that

and

Thus, we obtain an explicit solution of and for load flowequations (13)(14) in terms of generator angle, . Substitutingthis local solution into (11)(12) yields equivalent ordinary dif-ferentialequations locally. Conversely, if the is singular, thenthe system will lose load bus voltage causality. The model willthen collapse and voltage behavior can no longer be predicted.

B. Piecewise Constant Current Load Equivalent (Current Load Equivalent)

While the DAE description is accurate for the adoptedtransient dynamics model, it becomes complex with increasingcomputation cost as the size of the studied system becomes

large. Since the number of algebraic equations is usuallymuch larger than the number of differential equations in atypical power system, there will be a significant reduction of computing burden if one can obtain a local ordinary differentialequation equivalent for the original DAE.

We first present one of two equivalencing techniques whichcan eliminate the algebraic equations such that only internalgenerator buses are preserved, while approximately retainingthe characteristics of ZIP loads. This is achieved by repre-senting the ZIP loads with piecewise constant current loads.In a region where the loads are constant current sources, theycan be moved to the generator buses using techniques fromcircuit theory. Whenever the generator angles move beyond the

boundary of the moving hypercube (which defines the regionin generator angle space) centered at previous update trajectorypoint, we update the load current injection by solving theload flow equations. The piecewise linear approximation of anonlinear load model has also been suggested in [ 14], althoughthere are significant differences between the two approaches.

We use the following example to illustrate the above con-cepts. The sample power system consisting of two generatorsand several ZIP loads can be reduced to two generators, each of which has an additional current injection at its bus, accountingfor the effects of load current injection, as shown in Fig. 2. Thetwo additional current injections are functions of the machineangles one and two.

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Fig. 2. Sample power system and current load equivalent.

The following are details of the piecewise equivalencingprocess. For the original system, the bus current phasors arerelated to the bus voltage phasors and bus admittance matrixthrough

(15)

where thesubscript denotes the load buses to be eliminated andthe subscript denotes the internal generator buses that are tobe preserved. After simple algebraic manipulations, the current-voltage relationship equation (15), reduces to

(16)where

(17)

(18)

Equation (16) has two terms, with the first term representingthe contributions to generator current injections from generatorvoltagesand thesecond term representing theload current'scon-tributions. By using (16), the swing equations for the th gener-ator can be readily derived as

(19)

(20)

where



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and are the entries of matrix and arethe entries of matrix . Note that the load currentis, in fact, a function of generator angles due to the ZIP loads.The principle of the technique is that we approximate the loadcurrent, which is a function of generator angles, by a constantvalue whenever the generator angles lie inside the moving hy-percube along the projected trajectory on generator angle space.

The hypercube is a set of generator angles defined in anglespace as

where the is an update parameter with unit radian or degreespecified by monitors, and is the angle of last update trajec-tory point. Fig. 3 illustrates the moving hypercube along the trajectory. With the approximation of constant current load in themoving hypercube and update of currents whenever exceedingthe hypercube, the transient dynamics are then described bypiecewise swing (19)(20). When the generator angles movebeyond the boundary of the current hypercube, the constant

load current will be recomputed by the following procedures.First, we choose the current generator angles as the new centerof the moving hypercube and then solve load flow equations(13)(14) using current generator voltage magnitudes and an-gles as knowns for load bus voltage magnitudes and angles ata given load bus. Then, the new constant load at the th bus iscomputed as

where is the power factor angle at bus given by

This completes the equivalencing process.

C. Piecewise Constant Transfer Admittance Equivalent (Admittance Equivalent)

In this subsection, we present the other equivalencing tech-nique which can eliminate algebraic equations so that only thedifferential equations describing the dynamics of internal gen-erator angles are preserved, while approximately retaining the

nonlinear characteristics of ZIP loads. This is achieved by ab-sorbing the ZIP loads into piecewise constant transfer admit-tance. Fig. 4 shows that sample power system consisting of twogenerators and several ZIP loads. This power system can be re-duced to two generators, each of which is connected by trans-mission line with equivalent admittance and incremental equiv-alent admittance accounting for the nonlinear characteristics of the ZIP load.

The equivalencing technique is described as follows. First,from circuit theory, one obtains

(21)

Fig. 3. Moving hypercubes in the current load equivalent technique.

After algebraic manipulations, (21) yields(22)

where is a function of voltage magnitude at load buses andis derived as

(23)where

(24)

When the generator angles move beyond the boundary of thehypercube centered at the previous update trajectory point, theequivalent admittance is updated by first-order approxima-tion to avoid matrix inversion. Assume that is prefault loadvoltage magnitude and is incremental load voltage magni-tude due to angle swing, which can be obtained by solving load

flow. A first-order Taylor expansion for is ob-tained as

(25)

where

and

By using (25), the swing equations for the th generator can bereadily derived as

(26)

(27)

where



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Fig. 4. Sample power system and admittance equivalent technique.

and

where are the entries of andare the entries of .

With the approximation of equivalent admittance in the hy-percube and update of equivalent admittance whenever the hy-percube is exceeded, the transient dynamics are then describedby piecewiseswing (26)(27). Thiscompletes the equivalencingprocess.

D. Criteria for Transient Stability Prediction

This section establishes the criteria for stability identificationof a developing swing. Traditionally, transient stability is con-cerned with the capability of generators to capture synchronismfollowing a large disturbance, such as three-phase short circuit,loss of generator, or sudden change of load. In this case, thecriteria can be so defined that a swing after a fault is deter-

mined as an unstable swing if the relative angles of differentgenerator groups separate indefinitely with times. Otherwise,it is identified as a stable swing. However, in practice, a post-fault swing which does not cause a synchronism problem mayexhibit pole slipping, voltage dip, and transmission line over-loading. These conditions may initiate important relay opera-tions such as out-of-step blocking or tripping, thus causing un-desirable situations. Therefore, a more stringent stability crite-rion is needed to meet real-time operation requirements and wedefine the practical transient stability criterion in terms of thesecurity constraint region in statespace for prediction use.

1) Generator Angle Security Constraint: Considerations of out-of-synchronism and pole-slipping conditions impose limits



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on the machine angles of internal buses with respect to neigh-boring buses as

Denote the security region inside the limits by , i.e.,

2) Generator Frequency Deviation Constraint: The fre-quency deviation that accompanies system disturbances iscaused by the imbalance between load and generation. Excessgeneration will cause a rising frequency. Excess load, however,will cause the frequency to drop. Both cases can cause perma-nent damage to steam turbines. Therefore, limits are imposedon the generator frequency deviation as

Denote the region inside the limits by , i.e.,

3) Load Bus Voltage Magnitude Security Con-straint: Consideration of operating and voltage dip impose thelimits on load bus voltage magnitude as

Denote the corresponding security region in space by ,i.e.,

4) Load Bus Voltage Angle Security Constraint: Thermalconsiderations limit the amount of current flowing throughtransmission lines and transformers. By calculations andapproximations, the line flow constraints may be expressedapproximately in terms of phase angle differences, implyingthat there are limits imposed on load bus voltage angles as

Denote the security region in space by , i.e.

5) Practical Transient Stability Criterion: We define the se-curity set in space as

With the preceding concepts, we precisely formulate the

real-time transient stability prediction problem in the followingway.Problem: Given a set of real-time measurement

vectors whereis the sampling period and

is the length of the observation window after a fault. For ,the measurement vector comprises the prefault steady state. For

, the measurement vectors comprise the post-fault observed dynamic states. Usually, stability prediction isbased on an observed eight-cycle window of real-time measure-ments. Three consecutive measurements, four cycles apart, aretaken, that is, . The objective is to find a methodologythat can process the observed real-time measurements to calcu-

late the time history of over a prediction window

with length before the transient swing actually occurs,so that one can predict whether a developing swing is stable ornot by the following criteria.

Stable swing:, for , otherwise, the

swing is determined to be unstable. Remark: In practice, since there are uncertainties in the

model and noise in the real-time measurements, it is saferto predict the stability of a swing by processing consecutiveobserved measurements as different initial conditions and goingthrough the methodology in turn. When these are complete, theoutcome of stability prediction is checked. If the number of stability status is greater than the instability status, the swingis identified as being stable. Otherwise, it is determined to beunstable.

In the following two sections, we propose three method-ologies, decoupled integration, current load equivalent, andadmittance equivalent for solving the above stability predictionproblem.

III. IMPLICITLY DECOUPLED PQ INTEGRATION (DECOUPLEDINTEGRATION ) TECHNIQUE

Numerical schemes for simultaneous solution of DAE's arecurrently available [ 15]. The simplest one, using the implicittrapezoidal rule, has proven to be numerically stable, as well ashaving only minor errors for solving DAE's, such as the well-known Dommel's technique [ 16]. This schemeinvolves formingan update matrix and performing matrix factorization, such asLU decomposition, at several time steps. Hence, the implicit in-tegration methods are often considered to be computationally

more expensive than explicit integration methods such as theRungeKutta method. Using the proposed decoupled integra-tion method, it is demonstrated both theoretically and numer-ically that, under certain conditions, a constant and decoupledform of the update matrix can be used to speed up the implicitintegration technique. Specifically, the algorithm is shown to beconvergent under some reasonable assumptions.

A. Decoupled Integration Algorithm

The decoupled integration technique is outlined below.First we define the following functions for the ease of state-

ments in the algorithm

(28)

(29)

(30)

(31)

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Step 1) Discretize (11)(14) by the implicit trapezoidal rule,yielding

(32)

(33)

(34)

(35)

where is the step size and the subscripts anddenote the time index of two successive trajectory

points. The postfault trajectory in state space is anordered set of trajectory points, i.e.,

which satisfies (32)(35) recursively. Partition func-tions (28)(31) into two groups. Group A consistingof functions (28)(30) and Group B consisting of function (31).

Step 2) Evaluate the Jacobin matrix of Group A withrespect to at the prefault operating point,

. Note that we do not need to assumeknowledge of the fault location

(36)

and also the Jacobian matrix of Group B withrespect to

(37)

Step 3) Find the next trajectory pointby quasi-Newton

method using as the initial point.Setting

the quasi-Newton iterations are

(38)

(39)

until the convergence condition issatisfied.

Step 4) Perform Step 3) to compute successive trajectorypoints until the prediction intervalis finished, or until convergence does not occur forsome value of . If convergence fails to occur, e.g.,

for , then divide the step size by 2 andrepeat Steps 2) and 3), usingas the initial condition. If convergence still doesnot occur, then check the singularity of at

. If is nearly singular, thenstop the algorithm and give the prediction of voltageinstability. If is nonsingular, reduce the step size

until convergence occurs in Step 3). Repeat Step 4)until the prediction interval is finished or voltageinstability is determined.

Remark 3.1: The advantages of this technique are the nu-merical stability of implicit integration and a large reduction of computational burden because there is no new function evalua-tion during Steps 3) and 4).

Remark 3.2: In our numerical experiments, we required onlyone evaluation for & evaluated atthe prefaultcondition inorder to predict several seconds into thefuture. This implies thatwe can compute various & and store them in the prefaultstage for different configurations, so that computing time can besaved in the postfault prediction stage.

Before demonstrating the effectiveness of the above algo-rithm by numerical experiments, we would like to theoreticallyshow the feasibility of the algorithm by the following lemmasand theorems.

Define the system Jacobin of (28)(31) with respect toas follows:

Lemma 3.1 (Existence of the Successive TrajectoryPoint):

If is evaluated as nonsingular at a trajectorypoint, , then there exists an unique successivetrajectory point , which can beexpressed as function of for sufficiently small .

Proof: Note that

and

So,

Hence, the result follows from the implicit function theorem. Lemma 3.2 (Nonsingularity of & ): If the following

conditions are satisfied:



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then the iteration performed in Step 3) converges to the succes-sive trajectory point starting from

. Remark 3.3: Although Condition iii), which is a complex

function of both step size and system structure itself, is difficultto check, our numerical experiments show that most cases willconverge by reducing the step size. Thus, in practice, there is no

need to directly check Condition iii) before integration. Instead,we put an adaptive step size adjustment procedure in Step 4) of the algorithm in order to obtain convergence.

IV. PIECEWISE EQUIVALENTS TECHNIQUES

Basically, there are two kinds of computations used in piece-wise constant current load equivalent (current load equivalent)and piecewise constant transfer admittance equivalent (admit-tance equivalent), namely, fourth-order RungeKutta integra-tion and decoupled power flow calculation.

1) Piecewise Constant Current Load Equivalent (Current Load Equivalent): The fourth-order RungeKutta integration

is used to integrate the piecewise swing equations (19)(20).The decoupled power flow method is used for updating equiv-alent load current injections whenever the generator anglesexceed the boundary of the moving hypercube. Since the loadbus voltages (magnitudes and angles) do not appear explicitlyin the swing equations, they must be updated using a sensitivityanalysis.

The load bus voltages can be computed by solving the lin-earized load flow equations as follows. By linearizing the loadflow equations about the base trajectory point centered in thecurrent hypercube, the real and reactive power increments canbe expressed in terms of the voltage magnitude increments, thephase angle increments, and the Jacobian matrix . In matrix

form we have

(40)

Under the assumptions of constant generator voltage magni-tudes and ZIP loads, (40) reduces to

(41)

Rearranging (41) to solve for and yields

(42)

where

(43)

Since a load flow is performed at each update of load currentin the current load equivalent method, the Jacobian matrices in(40) are available for the base trajectory point in the current hy-percube. However, it is typical to compute at the prefault op-erating point. This does not result in a severe loss of accuracy,whereas the savings in CPU time are considerable. Thus, (42)

expresses the load bus voltage magnitude and angle incrementsas a known function of the generator angle increments. There-fore, the load bus voltage magnitude and angle variations canbe monitored in terms of the generator angles within the hyper-cube.

The current load equivalent technique for real-time transientstability prediction can be summarized as follows.

Step 1) Set up the practical transient security region,and update parameter . Then compute quantities

and at prefault steady operating con-dition.

Step 2) Integrate the swing equations [(19) and (20)] andsolve the linearized load flow equation [(42)]one step ahead by using arrived phasor measure-ment. Check whether the one-step prediction value

lies withinand predict instability in the case that it does not.If instability has not occurred, the one-step pre-diction value is checked to see whether it exceedsthe boundary of the current hypercube. If it does,recompute the load currents and update the swingequations. Otherwise, no update is made.

Step 3) Repeat Step 2) until either the prediction interval isfinished or instability is determined.

2) Piecewise Constant Transfer Admittance Equivalent (Ad-mittance Equivalent): Here, similar to the current load equiva-lent technique, the fourth-order RungeKutta integration is usedto integrate the piecewise swing equations ((26) and (27)). Thedecoupled powerflow is employed to update the transfer admit-tance matrix whenever the generator angles move beyond theboundary of the current hypercube. The load bus phasor varia-tions are approximated by (42).

The admittance equivalent technique for the real-time tran-sient stability prediction can be summarized as follows.Step 1) Set up the practical transient security region,

and update parameter . Compute the quantitiesand at the prefault operating

point.Step 2) Integrate the swing equations [(26) and (27)] and

solve the linearized load flow equation [(42)] onestep ahead. Check whether the one-step predictionvalue lies within

and predict instability in the case that it doesnot. If instability has not occurred, the one-step pre-diction value is checked to seewhether it exceeds theboundary of thecurrent hypercube. If it does, recom-pute the transfer admittances and update the swingequations. Otherwise, the transfer admittances re-main unchanged.

Step 3) Repeat Step 2) until either the prediction interval of interest is finished or else instability is determined.

V. NUMERICAL EXPERIMENTS

This section presents numerical simulations of the proposedtechniques, i.e., decoupled integration, current load equivalentand admittance equivalent, on two sample power systems. Thefirst system is a four-machine six-bus system with ZIP load



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Fig. 5. One-line diagram of the four-machine system.

TABLE IAVERAGE CPU T IME (SECONDS ) OF THE

DECOUPLED INTEGRATION TECHNIQUE AND THE FULL JACOBIAN METHOD FORTHE FOUR-MACHINE SYSTEM 1-S DYNAMIC RESPONSE

Fig. 6. Representative stable angle swing of Gen. 4 calculated by decoupledintegration and current load equivalent using step size, 0.02 s *: decoupledintegration, s +: current load equivalent ,s. : current load equivalent , s.

, taken from a sem-inal paper on transient stability regions [ 17]. The second samplepower system is the New England ten-machine, 39-bus systemwith ZIPload

. The programs are coded in FORTRAN and implemented

Fig. 7. Representative unstable angle swing of Gen. 4 calculatedby decoupledintegration and current load equivalent using step size, 0.02 s *: decoupledintegraion, s +: current load equivalent ,s : current load equivalent , s

on an HP 9000/720 workstation. There are two important met-rics, CPU timeand approximation error, for evaluating the effec-tiveness of the proposed techniques for the real-time predictionproblem. The CPU time was measured by placing timing rou-tine calls within the program. The first call was placed beforethe postfault computation (not including the computation of theprefault quantities such as and

and the second call was placed at the end. Theapproximation errors were measured by the following formula:

wheregenerator angle calculated by proposed tech-niques;

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Fig. 8. Representative stable angle swing of Gen. 3 calculated by decoupledintegration and admittance equivalent using step size, 0.02 s * : decoupledintegration, s. +: admittance equivalent ,s : admittance equivalent , s

Fig. 9. Representative unstable angle swing of Gen. 3 calculated by decoupledintegration and admittance equicalent using step size, 0.02 s * : decoupledintegration, s + : admittance equivalent ,s : admittance equivalent , s

generator angle calculated by ETMSP simula-

tion package [ 20];: -infinite norm.Note that we regarded the generator angles calculated by the

ETMSP as theaccuratevalues compared to generatorangles cal-culated by the piecewise equivalent techniques. The justificationis obvious because the ETMSP did not make any approxima-tions in the adopted zip load model.

1) TestSystem1: The four-machinesix-bus system is shownin Fig. 5. We treated Gen. 1 of the four -machine system as theslack bus in the initialization since it had the largest inertia con-stant. Three-phase short circuit to ground faults were simulatedto occur on various lines with two-cycle (1/30 s) fault clearingtimes. In the first phase of the simulation, we timed the speed of

Fig. 10. One-line diagram of ten-machine system.

the decoupled integration techniquewhen calculating stable andunstable swings caused by different fault locations. For compar-ison, we also measured the speed of integrating the same stableand unstable swings by implicit trapezoidal integration with afull Jacobian update at every time step and the same stop cri-teria as decoupled integration technique. This is based on thefact that the decoupled integration technique has the same ac-curacy as the full Jacobian method. The average timing resultsare shown in Table I, showing that the decoupled integrationtechnique is about threefour times faster than the integrationtechnique which uses a full Jacobian update at every time step.It is observed that the integration speed of the decoupled inte-gration technique is faster than the real power system responsetime (1 s) for both stable and unstable swings. Thus, the decou-pled integration technique can predict the stability of swings inreal time for the four-machine system.

In the second phase of simulation, we compared the perfor-manceof thedecoupled integration technique andthe two piece-wise equivalent techniques, current load equivalent, and admit-tance equivalent by timing the CPU time and measuring the ap-proximation errors. A representative 4 s stable transient swingwas integrated by the decoupled integration and current loadequivalent techniques using both no update and 60update for the current load equivalent method. Thecorresponding swing curves computed for Gen. 4 are shown inFig. 6. The CPU times are 0.7 s for the decoupled integration,

0.11 s for the current load equivalent , and 0.16 s for thecurrentloadequivalent . The approximation errors forthe current load equivalent and current load equiva-lent are and , respectively. Note that all threeintegration techniques can predict the swing in real time for thiscase. Moreover, it only takes 0.05 s more CPU time for the cur-rent load equivalent to reduce the approximation er-rors by about 60% from the current load equivalent .In Fig. 7, a representative 2 s unstable swing is shown for Gen.3. The CPU time of this case are 0.55 s for the decoupled inte-gration, 0.05 sfor the current load equivalent and 0.07s for the current load equivalent . This case is es-pecially significant since the current load equivalent

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TABLE IICPU T IME OF VARIOUS TECHNIQUES FOR 1-S DYNAMIC RESPONSE OF A

39-B US SYSTEM

TABLE IIIERRORS OF PROPOSED PIECEWISE EQUIVALENT TECHNIQUES FOR 1-S

DYNAMIC RESPONSE OF A 39-B US SYSTEM

calculation, which represents the typical constant current loadmodel, yields the wrong stability prediction, while the currentload equivalent takes only 0.02 s additional CPUtime and predicts thesame instability as the decoupled integra-

tion.We conducted similar simulations using transfer admittanceboth without update admittance equivalent and with60 updates admittance equivalent . The results areshown in Figs. 8 and 9. Again, it is significant that for the un-stable case the admittance equivalent predicts thewrong stability status while admittance equivalenttakes only 0.01 s additional CPU time to give same results asthe decoupled integration.

2) Test System 2: The IEEE ten-machine 39-bus system isalso used to illustrate the performance of our integration tech-niques. The one-line diagram of this system is shown in Fig. 10.Although this system is not very large, it is a good example

of a reduced-order system in an interconnected network, repre-senting the 345-kV transmission network of New England [ 18].Three-phase short circuits to ground faults were simulated to

occur on various transmission lines. The fault duration was twocycles, followed by tripping the appropriate circuit breakers.The postfault system configuration is the same as the prefaultsystem, except that the faulted line is eliminated. We timed theexecution time for the calculation of 1 s of postfault dynamic re-sponse and measured the approximation errors. Each techniquewas tested with different step sizes on an HP 9000/720 worksta-tion. Therepresentative simulation results areshownin Tables IIandIII, where it canbe seen that both current load equivalent andadmittance equivalent can predict the swings in real time with

an acceptable degree of accuracy. For example, the approxima-tion errors of current load equivalent and admittanceequivalent with step size (0.02 s) are less than .Although decoupled integration can not be faster than real-timeresponse for this test system, it would be possibe to speed up de-coupled integration to achievereal-time prediction by upgradingthe workstation used for simulation. By increasing the step size,

we achieved a faster calculation, although the approximation er-rors became larger. Moreover, in this test system we found thatthe decoupled integration would diverge if we used a step sizelarger than 0.06 s for most fault locations.

VI. CONCLUDING REMARKS

Decoupled integration, current load equivalent and admit-tance equivalent techniques were proposed and tested on twosample power systems to show solutions for a real-time tran-sient stability prediction problem. Depending on the nature of the system, one of these three techniques may be selected toprovide increased speed as well as accuracy. If there are severalcomputers connected by network in the control center, then theproposed three techniques can be sped up by paralleling the al-gorithms. Since the update matrices and are constant indecoupled integration, decoupled integration is inherently par-allel. For the two piecewise techniques, parallel RungeKuttaschemes are already available from the literature. Therefore,greatly improved performance of computer-based transient sta-bility prediction can be expected in actual situations by usingthe proposed techniques.

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Chih-Wen Liu (S93M96) was born in Taiwan in 1964. He received the B.S.degree in electrical engineering from National Taiwan University, Taipei, in1987 and the Ph.D. degree in electrical engineering from Cornell University,Ithaca, NY, in 1994.

Since 1994, he has been with National Taiwan University, where he is an As-sociate Professor of electrical engineering. His research interests include powersystem computer applications.

James S. Thorp (S58M63SM80F89) received the B.E.E., M.S., andPh.D. degrees from Cornell University, Ithaca, NY.

He joined the faculty of Cornell in 1962, where he is currently the CharlesN. Mellowes Professor in Engineering and Director of the School of ElectricalEngineering.In 1976,he wasa Faculty Intern with theAEP Service Corporation.In 1988, he was an Overseas Fellow at Churchill College, Cambridge, U.K.

Dr. Thorp was an Associate Editor of the IEEE T RANSACTIONS ON CIRCUITSAND SYSTEMS from 1985 to 1987. He is a member of the National Academy of Engineering and is member of the IEEE Power System Relaying Committee,CIGRE, Eta Kappa Nu, Tau Beta Pi, and Sigma Xi.

new methods for computing power system dynamic

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