ansient stability prediction methods

7/28/2019 ansient Stability Prediction Methods

http://slidepdf.com/reader/full/ansient-stability-prediction-methods 1/7

Review on Transient Stability Prediction Methods

based on Real Time Wide-area Phasor Measurements

Xiaochen WuScience Research Institute

China Southern Power Grid Company

Guangzhou, P. R. China

Jinquan ZhaoResearch Center for Renewable Energy Generation

Engineering

Hohai University, Ministry of Education

Nanjing, P. R. China

Aidong Xu

Science Research Institute

China Southern Power Grid Company

Guangzhou, P. R. China

Hui Deng, Peng Xu

Research Center for Renewable Energy Generation

Engineering,

Hohai University, Ministry of Education

Nanjing, P. R. China

Abstract —With the wide application of PMU in power system, the

real time prediction and emergency control of power system

transient instability using the wide-area measurements has

brought wide attention. There are two sub-problems: the post-

fault rotor-angle trajectory prediction and the real time transient

instability detection. There are three categories for the former

sub-problem. They are the network reduction based rotor-angle

trajectory super-real time simulation, the curve-fitting based

trajectory extrapolation and the angle speed prediction and

integration methods. The curve-fitting based trajectory

extrapolation includes the polynomial function, the auto

regression model and the trigonometric function model. There

are three types of criteria for the transient instability detection.

They are the threshold-type criteria, the differentiation-typecriteria and the integration-type criteria. The threshold-type

criteria include the rotor angle threshold and the angle speed

threshold and the post-fault voltage trajectory based criteria. The

differentiation-type criteria include the trajectory concave and

convex characteristics based criterion and the instability

criterion based on the variation rate of energy function. The

integration-type criterion mainly is the EEAC theory based

instability criterion. A comprehensive comparison, analysis and

reviews of the fundamental properties of these methods are

presented in this paper.

Keywords-Real Time Transient Instability Prediction; Phasor

Measurement Unit; Rotor-angle perturbed trajectory prediction;

Curve Fitting based Trajectory Extrapolation; Trajectory Concaveand Convex Property; Post-fault Voltage Trajectory based criterion

I. I NTRODUCTION

The transient instability still is one of the biggest threats for modern power systems. With the wide application of phasor measurement unit (PMU) and Wide Area Measurement System(WAMS) in power system, the real time early-warning andemergency control of power system transient instability basedon PMU/WAMS has been one of the hot research issues [1-5].

The transient instability real time prediction based on widearea measurements can be divided into two sub-problems. Oneis the perturbed rotor angle trajectory prediction problem.Another is the transient instability detection problem based onthe observed or predicted post-fault trajectories. Generally,

power system emergency control requires a phase of time inadvance for its effectiveness, so the super-real-time predictionof the post-fault trajectory is necessary. So the perturbedtrajectory prediction is important. At the same time, theemergency control cannot be realized by only obtaining thecomplete perturbed trajectory without an accurate transientstability criterion. So the latter sub-problem is more important

and they are interrelated. On the other hand, the techniques for solving these two problems can be totally different. Their researches can be independent of each other.

A detailed classification and comprehensive review on thetechniques related to the real time prediction of transientinstability are presented in this paper. The advantages anddisadvantages of these methods are described and the futureresearch directions are discussed.

II. R EVIEWS OF METHODS OF R OTOR A NGLE PERTURBED

TRAJECTORY PREDICTION

The existed methods of rotor angle perturbed trajectory prediction based on wide area measurements can be classifiedinto three categories. They are the network dynamicequivalence based rotor angle trajectory super-real-timesimulation method, the curve-fitting based rotor angletrajectory exploration method and the angular velocity

prediction and integration method.

A. Network dynamic equivalence based rotor angle

trajectory super-real-time simulation method

J. Thorp et al. first investigated on the post-fault rotor angletrajectory super-real-time simulation based on the network dynamic equivalence [6-8]. This approach relies on the fact

This work was supported by National Natural Science Foundation of China (51077042).

978-1-4577-0365-2/11/$26.00 ©2011 IEEE 320



that a small sized equivalent system can actually be integratedmuch faster than real-time. Two piecewise dynamicequivalents are also proposed in [6], i.e., piecewise constantcurrent load equivalent and piecewise constant transfer admittance equivalent. The reduced-order model is derived byobserving the post-fault swing curve data for a fraction of asecond, and clustering the swing curves into coherent groups.The equivalent system is integrated using the most recent set of

phasor measurements as the initial condition, and the resultsare consistently accurate for approximately 1/2 second into thefuture. Both the high-speed computers and the parallelcomputing technique are important for this method. In addition,in an effort to reduce the computing time for integrating thedifferential-Algebraic equation (DAE) model of post-fault

power system dynamics, literature [6] also presented anImplicitly Decoupled PQ Integration technique. However, thismethod requires the data of network topology and parameters,which are hard to be satisfied in real power systems.

Literature [9] proposed an on-line identification of theadmittance parameters based perturbed trajectory predictionmethod. The Total Least Square algorithm was used for

parameter estimation of system post-fault configuration based

on PMU real time measurements. It eliminated the dependenceon the network topology and the model parameters. However,since the generators are kept, not been aggregated and reduced,the number of generators and therefore the number of

parameters for identification are very large. The requiredobservation time window is too large to be accepted.

Literature [10] proposed a perturbed trajectory predictionmethod in which on-line the coherent generator groups aredetermined on-line and the dynamic equivalence is done basedon the observed trajectories.

Literature [11-14] proposed a network order-reducingmethod of preserving the PMU installation nodes. Combined

with state estimation or power flow data, the power system isorder-reduced and simplified to such a system consisting of thegenerator nodes equipped with PMU. Once the power system is

perturbed, utilizing the measured data of PMU the relations between voltages and currents of the order-reduced nodes ismodified and substituted into corresponding generator modelsto realize the perturbed trajectory prediction of the power system. In this method the on-line or off-line determination of coherent generator groups are avoided.

The basic characteristics of the network dynamicequivalence based super-real-time trajectory integrationmethod are: either the network topology and parameters haveto be known, or the observation time window required for

identification of too many parameters is large. It is hard to berealized in a real power system.

B. Curve fitting based rotor angle trajectory extrapolation

There were 3 models used for rotor angle curve fitting inthe existed literatures. They are the polynomial function model,the auto regression model and the trigonometric function model.

1) Polynomial model based prediction method

In [15] Haque proposed that rotor angle and the angular velocity of the generator can be expressed through truncatedTaylor series expansions. Later on, Sun proposed that the rotor speed can be expressed by a polynomial function and thecorresponding parameters can be estimated by using the leastsquare method in [16]. With the application of PMU, [17-20]started to use the PMU measurements to estimate the model

parameters and to realize the real time prediction of the

perturbed rotor angle trajectory. The polynomial model can beexpressed as follows:

2

0 1 2( ) n

nt a a t a t a t δ = + + + +

" (1)

where ( )t δ

is the predicted value of rotor angle at time t .

0 1 2[ , , , , ]T

N n A a a a a= " is the parameter vector of the

model. n is the model order.

Let t Δ as the sampling period, the observation vector of

generator rotor angle is ( ) [ (0), ( ), , ( )]T

Y N t N t δ δ δ = Δ Δ" . The

parameter vector can be solved by using the least square

method.

( ) ( )T

N N A P H N Y N = ⋅ (2)

where

2

2

1 0 0 0

1 ( ) ( )( )

1 ( ) ( )

n

n

t t t H N

t N t N t

Δ Δ Δ=

Δ Δ Δ

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

"

"

" " " " "

"

(3)

1[ ( ) ( )]T

N P H N H N −= (4)

After the parameter vector N

A was estimated, the future

l step prediction value of rotor angle can be obtained through

(1):

0 1( ) ( )

1, 2, ,

n

nk t a a k t a k t

k N N N l

δ Δ = + Δ + + Δ

= + + +

"

"(5)

Once a new measurement is coming, use the rolling

prediction method to get the new observation vector

( 1)Y N + , and calculate the new parameter vector 1 N A

+

through (2).

In [15,16] the first swing stability limit of a power system

was determined through checking the existence of peaks of

rotor angles of the severely disturbed generators in the post-

fault period. The existence of peaks is checked by observing

the roots of time derivatives of rotor angles of these generatorsexpressed through the polynomial function.

2) Trigonometric function model based prediction method

Literature [21] presented a trigonometric function model based curve fitting method for perturbed trajectory prediction.

The trigonometric function model is:

0

( ) cos sinn n

n

t a nt b nt δ ∞

=

= +∑

(6)

321



where n is the model order,0 1 1[ , , , , , ]T

N n n A a a b a b= " is

the parameter vector of the model. The parameters can be

estimated by using the least square method.

3) Auto regression prediction method

Literature [22] proposed that a time sequence autoregression model was used to predict the perturbed rotor angle

trajectory. Literature [23] proposed that the PMUmeasurements were used to estimate the parameters of the

auto regression model. The auto regression model of the

generator angle is:

1 2( ) ( 1) ( 2) ( )nt t t t nδ α δ α δ α δ = − + − + + −

" (7)

where1 2

[ , , , ]T

N n A a a a= " is the parameter vector of the

model. [ ( 1), ( 2), , ( )]T

t t t t nϕ δ δ δ = − − −" is the measured

angle vector. n is the model order.

The observation vector is ( ) [ (1), (2), , ( )]T Y N N δ δ δ = " . The

parameter vector A can be estimated by using (2). The

observation matrix is:(0) ( 1) (1 )

(1) (0) (2 )( )

( 1) ( 2) ( )

n

n H N

N N N n

δ δ δ

δ δ δ

δ δ δ

− −

−

=

− − −

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

"

"

" " " "

"

(8)

Finally, the predicted values of generator rotor angle can beobtained by using the following equations.

1 1

1 1

1 1

( 1) ( ) ( 1) ( 1 )

( 2) ( 1) ( ) ( 2 )

( ) ( 1) ( 2) ( )

n

n

n

N a N a N a N n

N a N a N a N n

N l a N l a N l a N l n

δ δ δ δ

δ δ δ δ

δ δ δ δ

+ = + − + + + −

+ = + + + + + −

+ = + − + + − + + + −

"

"

"

"

(9)

Once the latest data ( 1) N δ + is obtained, use the rolling

least square method to update relevant parameter as followed:

11

1 11

N N N T

N N N

P K

P

ϕ

ϕ ϕ +

+

+ +

=+

(10)

1 1 1[ ( 1) ]T

N N N N N A A K N Aδ ϕ

+ + += + + − (11)

1 1 1

T

N N N N N P P K P ϕ + + +

= − (12)

4) Reviews on curve fitting based perturbed trajectory

prediction methods

The curve fitting based rotor angle trajectory extrapolationmethods do not need the knowledge of the network topology,model and parameters of power system and do not neednetwork dynamic equivalence. In addition, it has someadvantages such as requiring the least amount of data for estimating the model parameters, and producing tolerableerrors when predicting future behavior. It composed of 3 steps:determination of model order, the parameter identificationusing the least square method and the trajectory extrapolation.

The trigonometric function prediction is more suitable for stability fault cases, but not suitable for the instability fault

cases. The auto regression prediction method is relatively precise for the fault cases with monotonously changingtrajectories. However, it is sensitive to the start-up time of

prediction and the sampling period and therefore the stability of forecast accuracy is poor. The polynomial model based

prediction method has better applicability and accuracy.

C. The angular velocity prediction and integration method

Literature [25] first proposed that the generator angular velocity can be obtained by Newton interpolation method andthe generator angle in a future time can be calculated byintegration of the angular velocity.

Literatures [26, 27] presented a fast learning, on-linemethod for the prediction of power system transient instabilityand an example of its application to a single machine andinfinite bus. The proposed algorithm is adapted from a provenrobotic ball-catching algorithm, which includes the prediction

process and the tracking process. For instability prediction, the ball location is replaced by measured relative generator rotor angle. Using the measured relative rotor angle, the algorithm

predicts the rotor angle at a future time. Literature [28]

followed the idea of [26, 27] and did some tests on IEEE testsystems.

Since the generator rotor has considerable large inertia, thevariation of its angular velocity ω is a smooth procedure. In

order to predict the rotor angle, Taylor series expansion is usedto predict the angular velocity.

0 2 1 2 2 1 2( ) ( ) ( ) ( )( )T t t T t T t T t ω ω α α + = + − + − − (13)

where 0t ,

1t ,2t are the latest three measuring times.

0( )T t ω +

is the predicted angular velocity after a time period

of T . 1α and 2

α are as followed:

2 11

2 1

( ) ( )t t

t t

ω ω α

−=

− (14)

1 00

1 0

( ) ( )t t

t t

ω ω α

−=

−

(15)

1 02

2 0t t

α α α

−=

−

(16)

The generator rotor angle after a time period of T can beobtained by integration of (13).

0

00 0

2 210 2 0 0 1 2 0

3 3 2 2

2 0 1 2 0 1 2 0

( ) ( ) ( )

( ) ( )( ) ( ) ( )2

1 1[ ( ) ( )( ) ( )]3 2

T t

t T t t dt t

t t T t T t t T t

T t t t T t t t T t

δ ω δ

α δ ω α

α

+

+ = +

= + − + − − −

+ − − + − + −

∫

(17)

It should be pointed out that, even though there are two processes in the algorithm of robotic ball-catching presented inliteratures [26-28]: the tracking process and the predicting

process, the predicting process is used only for power systemtransient rotor angle prediction. The tracking process of therobotic hand is useless for perturbed trajectory prediction. Themain step of this method is the integration of the predicted

322



angular velocity. Therefore we call it the angular velocity prediction and integration method in our paper.

In addition, literatures [26-28] adopt Newton Interpolationto figure out the parameters, which is faster and more accuratethan through the least square method. However, in the real

power system, there are bad data from time to time because of sampling or communication errors. At that time, the leastsquare method would reduce the predicted error.

III. R EVIEWS ON METHODS OF TRANSIENT I NSTABILITY

DETECTION

The existed transient stability detection methods based onreal time wide area measurements can be classified into threecategories. They are the threshold value criteria, thedifferentiation-type criteria and the integration-type criteria.

A. The threshold value criteria

The existed threshold value criteria include the relativerotor angle threshold value criterion, the relative angular velocity threshold value criterion and the post-fault voltage

trajectory based stability criterion.

1) The relative rotor angle threshold value criterion

The relative rotor angle criterion is the classical transient

rotor angle stability indicator.

1

1

G

G

N

k k

k i i N

k

k

H

H

δ

δ δ δ =

=

= − ≤ Δ

∑

∑

(18)

where,k H is the moment of inertia of k th generator,

G N is

the number of generators.i

δ andi

δ are rotor angle and the

relative rotor angle of i th generator. δ Δ

is the thresholdvalue of relative rotor angle, say, 0100 .

2) The relative angular velocity threshold value criterion

Literature [29] proposed that the difference of the angular

velocity of a severe disturbed generator with the angular

velocity of system center of inertia (COI) can be used as the

indicator for transient instability assessment.

1

1

G

G

N

k k

k i i N

k

k

H

H

ω

ω ω ω =

=

= − ≤ Δ

∑

∑

(19)

where,k ω is the angular velocity of generator k .

iω andiω

are angular velocity and the relative angular velocity of i th

generator. ω Δ is the threshold value of angular velocity,

which is obtained by a great deal of off-line case simulations.

3) Post-fault voltage trajectory based stability criterion

In [30] C. W. Taylor et al. first proposed an idea that theperturbed voltage trajectories of some key buses are used todetect the transient instability. Based on this idea, a real widearea emergency control system called WACS was realized in

BPA system. The algorithm is simple, based on 12 voltagemagnitude measurements at seven 500-kV stations. Aweighted average voltage is computed from the 12measurements, with highest weight for measurements wherethe voltage swings are usually greatest. A nonlinear integratorcomputes volt-seconds below a threshold setting.Accumulation is blocked for voltage recovery. Control actionresults when the volt-second accumulation reaches a set-point;

also, the weighted voltage must be below 490 kV for generatortripping. Beneficially, faster operation results for more severedisturbances.

According to the idea of [30], literature [31] presented amethod that utilizes the post-disturbance voltage phasors topredict the system transient stability status and indicate inadvance when the power system is approaching a transientlyunstable condition.

4) Reviews on threshold value criteria The threshold value criteria are easy to be implemented.

The disadvantages are that they need a great deal of fault cases

to testify. The reasonable threshold values are hard to be set.

Therefore they are dependent on the off-line simulations andthe given fault set. For a changing-fast power grid they would

be either too conservative or miss-detected. They are not

adaptive to the different operation conditions.

B. Differentiation- type criteria

1) Stability criterion based on concave or convex

characteristic of perturbed trajectoryAn instability detection method based on generator angles,

angular velocities, and their rates of change was proposed in[32]. Transient instability or out-of-step condition is detected

by identifying the concave or convex characteristics of asurface on which the post-fault system trajectory lies.

Following this idea, a supposition was proved, and aninstability detection method is presented in [33, 34], which isindependent of the network structure, system parameters andmodel. According to the geometric characteristics of system

trajectory, a new index Δk is defined and used to identifysystem transient instability. Since the index is reliable andrequires less computational time, it can be applied to the designof out-of-step relaying.

( 1) ( )( 1)

( 1) ( )

i ii

i i

t t k t

t t

ω ω

δ δ

+ −+ =

+ −

(20)

( ) ( 1) ( ) 0i i ik t k t k t Δ = + − > (21)

where ( 1)i t δ + and ( 1)i t ω + are the measured values of

generator angle and angular velocity at time 1t + respectively.

In [35] a method was proposed to detect the instability

early by observing the geometric characteristic of post-fault

trajectory of the generalized one-machine equivalent system at

each time step. The mode of disturbance that best reflects

stability is determined and refreshed at each sampling time

step. Unlike the extended equal-area criterion (EEAC)

method, it does not require the computation of an unstable

equilibrium point. A sub-criterion of the concave or convex

characteristics on power-angle plane was added:

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( ) ( ) ( 1)( )

( ) ( 1)

d P P t P t P t

d t t

δ

δ δ δ

Δ Δ − Δ −′= Δ

− − (22)

2

2

( ) ( ) ( 1)0

( ) ( 1)

d P P t P t

d t t

δ μ

δ δ δ

′ ′Δ Δ − Δ −= = >

− −

(23)

( ) ( 1) P t P t β β ε = Δ − Δ − (24)

1 2( ) ( ) ( ) cos ( )sinc P t P t t t β λ β λ β Δ = − − (25)

where P Δ is the unmatched power introduced by the

disturbance.1 2( ), ( ), ( )c P t t t λ λ are the time-variance

parameters of the equivalent system. P β Δ is the unmatched

power of the equivalent system.Based on the dual-cluster equivalence method, the

geometric characteristic criterion of phase plane trajectory isapplied to the real-time prediction of transient instability for multi-machine system in [36]. The angle gap is used as anindex to choose the dominant mode search direction and theinstability conditions to stop the dominant mode search, whichmakes the dominant mode approaching the real instabilitymode step by step along with the trajectory evolution. The

equivalent trajectory prediction is carried out only when the parameter variation may weaken the system stability, and thegeometric characteristics of the predicted trajectory are used todetermine if the system is stable.

2) Stability criterion based on variation rate of single

machine transient energy A multi-swing stability assessment criterion based on

WAMS was proposed in [37]. The relationship between thevariation rate of transient unbalance energy and multi-swingstability with the single machine energy function was analyzed,and the corresponding characteristic of the variation rate of transient energy was presented in [37].

( ) [ ( ) ]i i i i mi ei i T COI idV dt H d dt P P H H P ω ω ω = − − −

(26)where

iV is the energy function of generator i ,

COI P is the

unmatched power of COI,T H is the ,

mi P andei P are the

mechanical power and the electromagnetic power of generator

i respectively.

The stability criterion of whole system is: if and only if allsevere perturbed generators in power system are determined asstable, the power system is stable. If else, than it is unstable.

The identification of the severe perturbed machines should be considered in terms of 2 aspects. The first one is that thevalue of the relative kinetic energy of machine in the early

phase of perturbation. The second one is that the value of the

variation of the generator electromagnetic power which reflectsthe topology variation of network. Both the speed error for computing the relative kinetic energy and the variation of generator electromagnetic power can be obtained from WAMS

platform.

The salient feature of this criterion is that the identificationof the severe perturbed machines replaces the identification of coherent generator groups. However, we think that theidentification method of the severe perturbed machines isheuristic and unsuitable for complex fault situations in a large

power system.

3) Reviews on Differentiation- type criteria The advantages of the differentiation-type criteria are that

they are developed from the transient stability theories andadaptive to all operation conditions and fault types. However,their shortcomings are that they are very sensitive to themeasurement errors. The filtering technique has to be used for handling the raw data. Since real power systems areintrinsically non-automatic and nonlinear, the quality of PMU

measurements cannot be assured and therefore the accuracy of these criteria is poor.

C. Integration-type criterion

Based on the EEAC methodology, [39] proposed anemergency EEAC method that is used to predict, analyze andestimate the degree of transient stability. The control action is aclosed-loop rolling process which is enlightened from thegeneral prediction control (GPC) theory. This scheme isapplicable to any complex fault scenarios and any detailelement models of power system. It could restrain themismatches for some unpredicted serious contingency in the

present emergency control and avoid a chain of events leading

to large-scale breakup.The extrapolation of power -angle curve is adopted to

predict the imaginary trajectory after the farthest point (FEP)and potential energy in [40]. Time-variation indices are

proposed to identify the cases with large error in extrapolation.

The advantages of the integration-type criteria are that theyare developed from the EEAC theory and adaptive to alloperation conditions and fault types. Its disadvantages are thatit needs the complete wide area measurements which cannot berealized recently for most of real power systems. In addition,the deceleration area of EEAC cannot be estimated accuratelyin advance. In a model-free situation, the stability of unstabletrajectories can be quantified exactly based on EEAC while the

stability quantification of stable trajectories faces greatdifficulty. Online identification for a system of highdimensions and time-variation is even more difficult [40].

IV. REVIEWS ON METHODS BASED ON INTELLIGENCE

ALGORITHMS

Based on artificial intelligence techniques such as the pattern identification, artificial neural network and fuzzyinference, the fast prediction of transient instability can berealized.

A radial basis function network based on fuzzy clustering(FCRBFN) and its learning algorithm is proposed in [41]. The

FCRBFN, whose inputs are simple function of generator rotor angles after fault measured by PMUs, is used to predicttransient stability of multi-machine power system. The learning

process is considerable fast and the neural network has highclassification precision.

A method for transient stability prediction is proposed in[42] based on the perturbed trajectories fitting of rotor angle. Inthis method, the system is simulated off-line and rotor angletrajectories are recorded under different disturbances, and thecharacteristic of trajectories are extracted by cluster analysis.The perturbed trajectory standard pattern database (PTSPD) is

324



thus obtained. The Euclidean distance between the real-timemeasured rotor angle trajectories and the central trajectory of one mode in PTSPD are calculated, and the rotor angletrajectories are forecasted by using a modified expression, thustransient stability is predicted quickly.

The main characteristics of these methods are that in order to obtain the parameters of the prediction model, a great deal of off-line simulations are required to produce the trainingsamples and then the validity of the prediction model has to betestified by using the non-sample set. They are similar to thetraditional the strategy table method. Even though they try toconstruct the strategy table by using the more systematicmethod and have certain interpolation capabilities, they havesome problems such as the mismatch of off-line simulationresults with real operation conditions. They do not come from

the stability mechanism.

V. CONCEPTUAL COMPARISON AND DISCUSSION

Most of the existed methods overviewed above aredependent on the complete wide area measurements, which

means PMUs are installed everywhere in power systems. For example, they need the rotor angle measurements of allgenerators in power grid for computing the system inertialcenter. However, the wide area measurements are incompletein a real power system. On the other hand, some of wide areameasurements such as the bus voltage phasor measurementsand the tie-line power measurements are ignored in thetransient stability real time prediction. We suggest that the

perturbed bus voltage trajectories are used for instabilitydetection instead of the perturbed generator rotor angletrajectories. The dynamics of bus voltage can represent the

total generators’ dynamics within the corresponding area The

wide area measurements on the tie-lines or cut-sets should beutilized for stability prediction.

All the proposed methods did not consider the coordinationwith the existed stability control devices which are designed asthe second line of defense, which means the functions of traditional stability control devices are replaced by thePMU/WAMS based stability control. We strongly suggest thatthe wide area measurements based stability prediction andcontrol should be a supplementary of the existed stabilitycontrol devices. Therefore, for prediction of the rotor angle

perturbed trajectory, once a new event is detected, the predictedtrajectory should be updated timely. For the stability detection,the effects of the installed stability devices should beconsidered.

VI.

CONCLUSION

The real time prediction of power system transientinstability using the wide-area measurements has brought wideattention. A classification of the techniques in this area and acomprehensive comparison, analysis and reviews on thesemethods are presented in this paper. The advantages anddisadvantages of these methods are described and somesuggestions are also given in our paper. We suggest that the

perturbed bus voltage trajectories are used for instabilitydetection besides the perturbed generator rotor angle

trajectories and in stability prediction and control the effects of the installed stability devices should be considered.

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