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AbstractPhasor Measurement Units (PMUs) have been

increasingly widespread throughout the power network. As a

result, several researches have been made to locate the PMUs for

complete system observability. Many protection applications are

based upon the PMUs locations. This paper introduces an

important application in power system protection which is the

detection of single line outage. In addition, a detection of the

outaged line is achieved depending on the variations of phase

angles measured at the system buses where the PMUs arelocated. Hence, a protection scheme from unexpected overloading

in the network that may lead to system collapse can be achieved.

Such detections are based upon an artificial intelligence

technique which is the support Vector Machine (SVM)

classification tool. To demonstrate the effectiveness of the

proposed approach, the algorithm is tested using offline

simulation for the 14-bus IEEE system.

Index TermsPhasor measurement units, PSCAD, Support

vector machines, Transmission line measurements.

I. INTRODUCTION

INE outage study provides a measure of the overall effect

on the system due to that line outage. The power flow ofthe system is affected by the line outage. Therefore, this may

lead to overloading for certain lines. Also, this causes changes

in the power angles for several buses. A protective action

should be made to prevent large system disturbances (over

loading of lines) leading to cascaded tripping results in a

system collapse. However, connectivity information over the

wide area is very important for system operations [1, 2].

In recent years, PMUs are the only devices which are

deployed over the entire power grid and are capable of

providing geographically dispersed accurate synchronized

measurements [3]. Therefore, they are used for system control,

protection, state estimation and topology estimation [4 - 8].

Consequently, PMUs became a powerful technology in power

system protection.

Mikolinnas and Wollenberg [9] have presented a version of

the Megawatt Performance Indices (PI) whish are function of

bus voltages and line flows and the corresponding limits. They

A. Y. Abdelaziz, S. F. Mekhamer and M. Ezzat are with Electrical Power and

Machines Department, Faculty of Engineering, Ain Shams University, Cairo,

Egypt.

E. F. El-Saadany is with Electrical and Computer Engineering Department,

University of Waterloo, Canada

include all terms in the infinite Taylors series expansion for

all the change in the performance index due to different

outage. Irissari and Sasson [10] have proposed an improved

computational procedure based on DC load flow method.

Vemuri and Usher [11] have presented a unified approach to

find sensitivity of performance index for single branch outage,

generation/load outage and combination of them. D. Hazarika

et al. [12] describe an algorithm for determining the line

outage contingency of a line taking into account of line overload effect in remaining lines and subsequent tripping of over

loaded line(s) leading to system split or islanding of a power

system using fast decoupled load flow analysis. J. Tate and T.

Overbye [13, 14] introduce two researches for single line

outage and double line outage respectively using phasor angle

measurements. This is done by utilizing an optimization

technique for event detection. Authors of [13] presented

accurate results for a line outage case for a certain line in the

37-bus IEEE system.

This paper focuses on the line outage problem and proposes

an artificial intelligence based technique for outage detection.

The outaged line is determined by means of an SVM

classification tool. The task of SVM is to utilize the outputinformation from the PMUs to determine a status for each line

if it is outaged or not. The PMUs calculate the phasor angles atthe certain buses based on the complete observability

approach discussed in [15]. Section II of this paper describes

how to use the SVM approach in line outage detection and

determination of outaged line. Section III proposes anapproach for detection of the outaged line based on the

variations of the phasor angles at different buses with the aid

of the SVM tool. Section IV introduces a numerical simulation

for the 14-bus IEEE system using an offline simulation

program EMTDC/PSCAD [19] and the mathematical analysiswith the aid of MATLAB. Section V clarifies the extracted

conclusions.

II. SUPPORT VECTORMACHINE

Support vector machine [16, 17] was originally introduced

by Vapnik and co-workers in the late 1990s. It is a

computational learning method based on the statistical

learning theory. SVM mainly has two classes of applications,

classification and regression. In this paper, only the

application of classification is considered.

The classification problem can be restricted to consideration

of the two-class problem without loss of generality. In this

Line Outage Detection Using Support Vector

Machine (SVM) Based on the Phasor Measurement

Units (PMUs) Technology

A. Y. Abdelaziz, S. F. Mekhamer, M. Ezzat and E. F. El-Saadany

L

978-1-4673-2729-9/12/$31.00 2012 IEEE

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Each of the six loading cases has an internal 20 cases of line

outage for the 20 lines in the system. Therefore, 120 vectors

are available to perform the required training of SVM.

The training vector consists of a number of parts arranged

together. Each part clarifies the angle variations at the buses

where the PMUs are located. The next step is to apply a

different loading case to test the SVM including all possible

line outages. Then, the results are tabulated and the accuracy

is calculated. It is proposed to perform such testing atconditions that differ from the training conditions.

Firstly, to study the importance of complete observability of

the power system, the training and testing are performed by

considering two, three and four PMUs. Table I gives detailed

results for a study case where the load at bus 14 is increased

25% than the normal loading in the standard IEEE 14-bus

system and two PMUs located at buses 2 and 10 are used.

Table II gives similar results while three PMUs at buses 2, 7,

and 10 are utilized. And Table III shows the same analysis

with four PMUs located at the buses 2, 7, 10, and 13.

Secondly, In order to ensure the superiority of the SVM

classification tool in the field of line outage detection, several

study cases have been studied including the complete

observability principle. The study cases are given as follow:

Study case (1): An increase of 25% in loading at bus 14.Study case (2): An increase of 25% in loading at bus 3.Study case (3): An increase of 40% in loading at bus 9.The targets of studying more study cases are; (1) to get an

approximated accuracy of the proposed approach, (2) to select

the appropriate range of penalty error for each line outage

detection case.

The results of the first study case are given in Table III,

while the other study cases are presented in Table IV and

Table V. The results are compared, and the comparison is

introduced in Fig. 5.

It is clear from Tables I, II and III that for each line, there is

a detectable range of penalty error "C" for the line outage.

When two PMUs are used, many lines are subjected to lack of

classification, either no detection or a miss of classification,

the accuracy in this case reaches a 16 correct classificationover 20 line outage cases. The remaining four cases are for

lines far away from the PMUs locations that results in a wrong

detection. The accuracy is improved when using three PMUs

and the correct detections reach 19 detections over 20 line

outage cases. Finally, when four PMUs are used and located

upon the complete observability approach, a complete

detection scenario is achieved and the correct detections reach

20 detections over 20 line outage cases and with a more wide

range of penalty error.

It is clear from Fig. 5 that for each line, there is a detectable

range of penalty error "C" for the line outage. Some lines are

classified clearly even the value of C is small which means

narrow range of classification or large which means wide

margin of classification (e.g. line connecting bus 6 and bus

13), where the result is correct for all tested range of C. While

other line needs a specified range of C (e.g. line connecting

bus 6 and bus 12), it requires a greater value of C starting from

1x104. On the opposite, some lines need small values of C

(e.g. line connecting bus 9 to bus 14), it requires lower value

of C up to 10, after that a miss-classification may occur and

the result becomes confusing.

TABLE I

RESULTS OF STUDY CASE (1) WITH 2PMUS LOCATED AT BUSES 2 AND 10

Study Case (1): An increase of 25% at bus 14 and PMUs are located at buses 2 and 10

Outage

Case

Result at

C = 1

Result at

C = 10

Result at

C = 100

Result at

C = 1000

Result at

C = 1x104

Result at

C = 1x105

Result at

C = 1x106

Result at

C = 1x107

Line 1-2 No outage 1-2 1-2 1-2 1-2 1-2 1-2 1-2

Line 1-5 No outage No outage 1-5 1-5 1-5 1-5 1-5 1-5

Line 2-3 No outage No outage 2-3 2-3 2-3 2-3 2-3 2-3

Line 2-4 No outage 2-4 2-4 2-4 2-4 2-4 2-4 2-4

Line 2-5 No outage No outage No outage No outage 2-5 2-5 2-5 2-5

Line 3-4 No outage No outage No outage No outage No outage No outage 3-4 3-4



Line 4-9 No outage No outage No outage No outage No outage No outage No outage 4-9

Line 5-6 No outage No outage No outage No outage No outage 5-6 5-6 5-6

Line 6-11 6-11 6-11 6-11 6-11 6-11 6-11 6-11 6-11Line 6-12 No outage No outage No outage No outage No outage No outage No outage No outage


Line 7-8 No outage No outage No outage No outage No outage No outage No outage No outage



Line 9-14 No outage 9-14 9-14 9-14 , 1-5 9-14 , 1-5 9-14 , 1-59-14, 7-9,

1-5, 6-13

9-14, 7-9,

1-5, 6-13Line 10-11 No outage No outage No outage No outage No outage 10-11 10-11 10-11



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TABLE II

RESULTS OF STUDY CASE (1) WITH 3PMUS LOCATED AT BUSES 2,7, AND 10

Study Case (1): An increase of 25% at bus 14 and PMUs are located at buses 2,7, and 10

Outage

Case

Result at

C = 1

Result at

C = 10

Result at

C = 100

Result at

C = 1000

Result at

C = 1x104

Result at

C = 1x105

Result at

C = 1x106

Result at

C = 1x107

Line 1-2 No outage 1-2 1-2 1-2 1-2 1-2 1-2 1-2

Line 1-5 No outage 1-5 1-5 1-5 1-5 1-5 1-5 1-5

Line 2-3 No outage No outage 2-3 2-3 2-3 2-3 2-3 2-3Line 2-4 2-4 2-4 2-4 2-4 2-4 2-4 2-4 2-4


Line 3-4 No outage No outage No outage 3-4 3-4 3-4 3-4 3-4


Line 4-7 4-7 4-7 4-7 4-7 4-7 4-7 4-7 4-7



Line 6-11 6-11 6-11 6-11 6-11 6-11 6-11 6-11 6-11




Line 7-9 7-9 7-9 7-9 7-9 7-9 7-9 7-9 7-9


Line 9-14 9-14 9-14 , 3-49-14, 3-4,

1-5

9-14, 3-4,

1-5

9-14, 3-4,

1-5

9-14, 3-4,

1-5

9-14, 3-4,

1-5, 6-13

9-14, 3-4,

1-5, 6-13Line 10-11 No outage No outage No outage 10-11 10-11 10-11 10-11 10-11



TABLE III

RESULTS OF STUDY CASE (1) WITH 4PMUS LOCATED AT BUSES 2,7,10, AND 13(COMPLETE OBSERVABILITY)

Study Case (1): An increase of 25% at bus 14 and PMUs are located for complete observability

Outage

Case

Result at

C = 1

Result at

C = 10

Result at

C = 100

Result at

C = 1000

Result at

C = 1x10

4

Result at

C = 1x10

5

Result at

C = 1x10

6

Result at

C = 1x10

7

Line 1-2 No outage 1-2 1-2 1-2 1-2 1-2 1-2 1-2

Line 1-5 No outage 1-5 1-5 1-5 1-5 1-5 1-5 1-5

Line 2-3 No outage 2-3 2-3 2-3 2-3 2-3 2-3 2-3

Line 2-4 2-4 2-4 2-4 2-4 2-4 2-4, 10-11 2-4, 10-11 2-4, 10-11


Line 3-4 3-4 3-4 3-4 3-4 3-4 3-4 3-4 3-4


Line 4-7 4-7 4-7 4-7 4-7 4-7 4-7 4-7 4-7



Line 6-11 6-11 6-11 6-11 6-11 6-116-11,

10-11

6-11,

10-11

6-11,

10-11

Line 6-12 No outage No outage No outage No outage 6-12 6-12 6-12 6-12Line 6-13 6-13 6-13 6-13 6-13 6-13 6-13 6-13 6-13


Line 7-9 7-9 7-9 7-9 7-9 7-9 7-9 7-9 7-9


Line 9-14 9-14 9-14 9-14, 3-4 9-14, 3-4 9-14, 3-4 9-14, 3-4 9-14, 3-4 9-14, 3-4



Line 13-14 13-14 13-14 13-14 13-14 13-14 13-14 13-14 13-14

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TABLE IV



Outage

Case

Result at

C = 1

Result at

C = 10

Result at

C = 100

Result at

C = 1000

Result at

C = 1x104

Result at

C = 1x105

Result at

C = 1x106

Result at

C = 1x107

Line 1-2 No outage 1-2 1-2 1-2 1-2 1-2 1-2 1-2

Line 1-5 No outage 1-5 1-5 1-5 1-5 1-5 1-5 1-5

Line 2-3 No outage No outage 2-3 2-3 2-3 2-3 2-3 2-3Line 2-4 2-4 2-4 2-4 2-4 2-4 2-4 2-4 2-4


Line 3-4 3-4 3-4 3-4 3-4 3-4 3-4 3-4 3-4


Line 4-7 4-7 4-7 4-7 4-7 4-7 4-7 4-7 4-7



Line 6-11 6-11 6-11 6-11 6-11 6-11 6-11 6-11 6-11


Line 6-13 6-13 6-13 6-13 6-13 6-13 6-13 6-13 6-13


Line 7-9 7-9 7-9 7-9 7-9 7-9 7-9 7-9 7-9

Line 9-10 No outage No outage No outage No outage 9-10 9-10 9-10 9-10Line 9-14 9-14 9-14 9-14 9-14 9-14 9-14 9-14 9-14



Line 13-14 13-14 13-14 13-14 13-14 13-14 13-14 13-14 13-14

TABLE V



Outage

Case

Result at

C = 1

Result at

C = 10

Result at

C = 100

Result at

C = 1000

Result at

C = 1x104

Result at

C = 1x105

Result at

C = 1x106

Result at

C = 1x107

Line 1-2 No outage 1-2 1-2 1-2 1-2, 10-11 1-2, 10-11 1-2, 10-11 1-2, 10-11

Line 1-5 No outage 1-5 1-5 1-5 1-5, 2-5 1-5, 2-5 1-5, 2-5 1-5, 2-5Line 2-3 No outage 2-3 2-3 2-3 2-3 2-3 2-3 2-3

Line 2-4 2-4 2-4 2-4 2-4 2-4, 2-52-4, 2-5,

7-8

2-4, 2-5,

4-9, 7-8,

2-4, 2-5,

4-9, 7-8,

Line 2-5 No outage No outage No outage No outage 2-5 2-5 2-5, 7-8 2-5, 7-8

Line 3-4 No outage 3-4 3-4 3-4 3-4, 10-11 3-4, 10-11 3-4, 10-11 3-4, 10-11


Line 4-7 4-7 4-7 4-7 4-7 4-7, 10-11 4-7, 10-11 4-7, 10-11 4-7, 10-11


Line 5-6 No outage No outage No outage No outage 5-6 5-6, 10-115-6,10-11,7-8, 9-10

5-6,10-11,7-8, 9-10

Line 6-11 6-11 6-11 6-11 6-11 6-11 6-11 6-11, 4-9 6-11, 4-9


Line 6-13 6-13 6-13 6-13 6-13 6-13 6-13 6-13 6-13Line 7-8 No outage No outage No outage No outage No outage No outage 7-8 7-8, 10-11

Line 7-9 7-9 7-9 7-9 7-9 7-9 7-9 7-9, 4-9 7-9, 4-9

Line 9-10 No outage No outage No outage No outage No outage 10-11 10-11, 7-8 10-11, 7-8

Line 9-14 No outage 9-14 9-14 9-14 9-14, 10-11 9-14, 10-11 9-14, 10-11 9-14, 10-11



Line 13-14 13-14 13-14 13-14 13-14 13-14 13-1413-14,

9-10

13-14,

9-10

7/28/2019 pmu_prot5

7/8

Fig

Fig

. 5 Comparison o

. 6 Phase angle va

PhaseangleatBus2

PhaseangleatBus10

different study c

riation for outage

0 0.1-20

-15

-10

-5

0

0 0.1-20

-15

-10

-5

0

ses using polyno

of line 6-13 at the

0.2 0.3

Time in seconds

0.2 0.3

Time in seconds

ial kernels.

instant t = 0.3 sec

0.4

0.4

Fig. 6

all pha

near b

decreas

accurat

Fig. 7

effect o

line 7-

accordioutage.

outage

from Fi

For t

than th

and (2).

case (2)

a miss

grey s

1x104.

40% o

cases e

permits

bus 10.

approa

detectio

The c

value o

for som

classifi

case o

process

subrout

"C" tha

to getof the S

cases o

onds for study ca

.5 0-20

-15

-10

-5

0

PhaseangleatBus7

.5 0-20

-15

-10

-5

0

PhaseangleatBus13

clarifies that

ors at all bus

s to the outa

e of about 4

e classificatio

shows that t

n the phase a

8 is normall

ng to the loaHowever, thi

of line 7-8 for

g. 5.

e study case

standard syst

. The differen

, the SVM fai

classification

adow. It det

The reason of

er loading le

specially near

a miss classi

Even a miss

h still has 1

n which mean

onclusion is t

f C increases

e cases the in

ations. There

f line outage

ing for all

ine operates

t to be adjust

orrect decisioVM classifica

er 60 cases w

se (1).

0.1

0.1

the outage of

s. But, the gr

ged line (i.e.o). Therefore,

in that case.

e outage of li

gle variations

y loaded wi

d flow studys may lead t

a great range

3), the bus nu

em that differ

e between th

ls in the detect

as shown in T

cts line 10-1

this miss cla

ts the study

the heavy lo

ication for the

classification

9 correct det

s a percentage

at the overall

to have much

rease in C all

fore, to have

, it is impo

lines with p

ith the suitabl

d through dif

ns. Consequetion tool for t

ith a percenta

0.2 0.3

Time in seconds

0.2 0.3

Time in seconds

line 6-13 has

eatest effect i

bus 13 phase

this result

e 7-8 is almo

. This happens

h a very s

before the ao a miss dete

of penalty err

mber 9 is 40

from both st

two study ca

ion of line 9-1

able V by bol

1 instead of

sification ma

ase far from

ded bus (i.e.

line connecti

ccurs, the ac

ctions versus

of 95 % of ac

accuracy inc

classification

ws an appear

correct deci

tant to mak

arallel subro

e or optimum

ferent study c

tly, the obtaiese study cas

e of 98.3%

0.4 0.5

0.4 0.5

7

an effect for

at the most

angle has a

elps for an

st having no

because the

all loading

tion of linection of the

or as noticed

overloaded

dy cases (1)

ses is that in

0 and makes

ded font and

-10 at C =

be that the

the training

bus 9). This

g bus 9 and

uracy of the

one wrong

curacy.

eases as the

margin. But,

nce of miss-

ion for any

a parallel

tines. Each

enalty error

ses in order

ed accuracys reaches 59

7/28/2019 pmu_prot5

8/8

Fig

de

th

re

(i.

en

pr

er

co

in

T

w

tra

be

wi

S

ne

cl

un

se

al

T

[1]

. 7 Phase angle va

he paper

termination o

technology o

ults when the

e. the PMUs

tire power net

ocessing for e

or. Therefore

rrect detectio

luding three s

e research c

en the regul

ining of SVM

so far from t

ll be achieve

M related to

twork dispatc

ssifications

loaded or ligh

ere action. F

he operating

orithm [18]

e amplitude a

US-Canada Po14, 2003 the Bl

riation for outage

V. CO

resents the

the outaged li

f PMUs. The

concept of c

are located i

work). The pa

ach line with

, the approa

s over 60 po

tudy cases for

n be applica

r daily loadi

. Hence, the a

e training cas

. It is suggest

he variations

center. The

ay occur du

t loaded line

ture work will

VI. AP

equations o

,

d phase angle

VII. RE

wer System Outaackout in the Uni

0 0.1-6

-5.5

-5

-4.5

-4

PhaseangleatBus2

0 0.1-17

-16.5

-16

-15.5

-15

PhaseangleatBus10

of line 7-8 at the i

CLUSIONSVM classi

ne in a power

proposed appr

mplete obser

certain buse

per suggests a

the optimum

h reaches an

sible cases of

the 14-bus IE

le for any n

g cases are

tual case of li

s. As a result,

ed to get a dy

f network loa

paper also sh

e to a case

hich cannot b

consider larg

ENDIX A

f the Rocke

of the sample

,

ERENCES

e Task Force. Fied States and Can

0.2 0.

Time in secon

0.2 0.

Time in secon

nstant t = 0.3 seco

ication tool

network based

oach has accu

ability is appl

s to observe

pplying a para

range of pen

accuracy of

outage scena

E power syst

twork especi

onsidered in

e outage will

a good detect

namic trainin

ding stated by

ws that the

of an outage

e considered

r systems.

eller and Ud

d signal are:

nal Report on Auada, 2004.

3 0.4

s

3 0.4

s

nds for study cas

in

on

ate

ied

the

llel

lty

59

ios

em.

lly

the

not

ion

of

the

iss

of

s a

ren

gust

[2] US-Imp

[3] B. Sof p

inco

Eng

[4] WeiEsti

Tra

[5] Geoad

mea152

[6] A.esti

Aug

[7] M.Proc

[8] E. LidenPo

[9] T.sele

no.

[10] G.met

Syst[11] S. V

algo

pp.

[12] D.anal

Ene

[13] J. E.mea

pp.[14] J. E.

angl

Proc

[15] S.meaon P

[16] S.Spri

[17] I. StSpri

[18] Q.mat

[19] PSC

0.5 0-15

-14.5

-14

-13.5

-13

PhaseangleatBus7

0.5 0-17

-16.5

-16

-15.5

-15

PhaseangleatBus13

(1).

Canada Power S

lementation of the

ingh, N. Sharma,hasor measureme

rporated with

ineering, Science

qing Jiang, Vijayator Utilizing

sactions on Powe

rge N. Korres, a

data processin

surements", Elect, March 2011.

onticelli, Mode

ation, IEEE T

. 1993.

ezunovic, Mon

. 39th Hawaii Int.

oureno, A. Costtification method

er Syst., vol. 21,

. Mikolinnas ,tion algorithm,

, pp. 608617, F

. Irissari, and A.

od for online s

., vol. PAS-100,emuri, and R. E.

rithms, IEEE T

46354, Feb. 19

azarika, S. Bhuyysis including th

gy Systems, vol.

. Tate, and T. J.

surements, IEE

16441652, Nov.. Tate, and T. J. Oe measurements,

eeding, pp. 15, J

hakrabarti, and

surement units foower Systems, vo

be, Support Vec

nger-Verlag, Lon

einwart, and A.

nger, 2008.. Wu, Z. Lu, T.

ematical Morop

AD Users Guide

0.1 0

Ti

0.1 0

T

ystem Outage T

Task Force Reco

A. Tiwari, K. Vet units (PMUs) i

ACTS controll

and Technology,

Vittal, and GeralSynchronized

r Systems, Vol. 2

d Nikolas M. M

g for system

ric Power System

ling circuit breake

ans. Power Syst.

itoring of power

. Conf. System Sc

a, K. Clements, adirectly based o

o. 4, pp. 192019

B. F. WollenbeIEEE Trans Powe

b. 1981.

. M. Sasson, An

ecurity analysis,

no. 4, pp. 18381Usher, On line

ans Power Appar

3.

n, and S.P. Chosystem islandin

28, no. 4, pp, 232

verbye, Line out

Transactions on

2008.erbye, Double lIEEE Power &

uly 2009.

E. Kyriakides,

r power system ol. 23, no. 3, pp. 1

tor Machines for

on, Ltd, 2005.

hristmann, Supp

Y. Ji, Protective

ology, Springer-V

Ver. 4.2, Manito

.2 0.3

me in seconds

.2 0.3

me in seconds

sk Force. Final

mmendations, 20

rma, and S. Singelectric power s

rs, Internation

ol. 3, no. 3, pp. 6

d T. Heydt, "A DPhasor Measure

, No. 2, pp. 563-

anousakis, "State

ncluding PMU

s Research, 81 (2

rs in weighted le

, vol. 8, no. 3,

ystem topology i

ences, Jan. 2006.

nd R. Cernev, Acollinearity tests

29, Nov. 2006.

rg, An advancer Apparatus Syst.

automatic contin

IEEE Trans Po

44, Apr. 1981.automatic contin

atus Syst., vol. P

dhury, Line outascenario Electr

243, May 2006.

age detection usi

Power Systems,

ine outage detectinergy Society G

Optimal place

bservability, IE331440, Aug. 2

Pattern Classific

rt Vector Machi

relaying of powe

erlag London Li

a Research Cente

0.4 0.5

0.4 0.5

8

Report on the

6.

, Applicationsstems network

l Journal of

482, 2011.

istributed Statements", IEEE

71, May 2007.

estimation and

and SCADA

11), pp. 1514-

st squares state

p. 11431149,

n real-time, in

topology error, IEEE Trans.

d contingency, vol. PAS-100,

ency selection

wer Apparatus

ency selection

AS-102, no. 2,

ge contingencyical Power and

g phasor angle

vol. 23, no. 4,

n using phasoreneral Meeting

ent of phasor

E Transactions08.

tion. England:

es, New York:

systems using

ited 2009.

r, April 2005.

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