Post on 03-Jun-2018
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1
UsesofIEEE1366and
CatastrophicDays
JohnMcDaniel
ViceChair DistributionReliabilityWG
LeadSeniorEngineer NationalGrid
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GoodbaselineforCatastrophicDaydiscussion
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.
Aclearerviewofperformance,bothona
a y as san
DuringMajorEvents Canformasolidbasisforreviewof
operationaleffectiveness,decisionmaking
andpolicymaking.
Moreconsistentbenchmarking.
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Definitionmustbeunderstandablebyallandeasyto
apply.
Definition must be s ecific and calculated usin thesameprocessforallutilities.
. Largeandsmall,urbanandrural.
SAIDIwaschosenastheindicator
becauseitissizeindependentand itisthebestindicatorofsystemstressesbeyondthose
thatutilitysstaff,buildanddesigntominimize.
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The2.5BetaMethodologyallowssegmentation
.
Onesetrepresentsthoseeventsofsuchareliability
toadequatelyrespond.(majorevents).
thoseeventsthatacompanyhasbuiltthesystemto
withstandandstaffedtores ondtoinamannerthat
doesnotrequireacrisismodeofoperation.(dayto
dayoperation).
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1. Collect values of daily SAIDI for five sequential years ending on the last day of the last complete
reporting period. If fewer than five years of historical data are available, use all available historicala a
2. If any day in the data set has a value of zero for SAIDI, do not include that day in the analysis.
. .
4. Find (Alpha), the average of the logarithms (also known as the log-average) of the data set.
5. Find Beta the standard deviation of the lo arithms also known as the lo -standard deviationof the data set.
6. Compute the major event day threshold, TMED, using the equation:
5.2 eTMED
7. Any day with daily SAIDI greater than the threshold value TMEDthat occurs during the subsequentreporting period is classified as a major event day.
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athresholdvalue,TMED thatisdeterminedby. .
For example, if TMED
= 3 minutes, than any day
declared a major event day
ct v t est atoccuronma orevent aysshouldbeseparatelyanalyzedandreported.
ot ng s xc u e
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.
willallowforconsistentcalculationofreliability
,
providecompaniesandcommissionswithamore
accurate indication of a Com an s controllable
servicequalityresults,
allowaclearreviewofcom an res onsetocrisis
modeevents,and
providealessdistortedindicationofthereliability
resultsforcompaniesofallsizes.
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applicationofIEEEStd.1366foundthat
changesintmedwhichoftenimpactthenext5
DistributionReliabilityWGformedaTask
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significantoutlierscouldimprovethemajoreventthresholdcalculationprocess
Severalmethodswereproposed
BetaVariables(Twooriginallycontemplated) BoxandWhiskers
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outlierdaystoensurethatsubsequent
performance,andisnottaintedbythe
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1)IEEEStd.1366
Leavethedefinitionasitcurrentlystands
2)BoufordheuristicMethod . ,
removedfromthedataset;thereafter,apply2.5methodasusual
3)Statistical
Box
n
Whiskers
method ,
outliers(bothhighandlow)inexcessof3timestheinnerquartilerange,removeanyhighorlowoutliersfromthedataset;thereafter,apply2.5methodasusual
Note:Inthepast,othermethods,includingrobustestimationwereconsidered
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Evaluatetheimpactofthesedays,onSAIDI,
Identifycompanieswithnocatastrophicdays
beingevaluated
, me , .foreachcompanyrepresented
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dataandholdthepotentialfornotproperlysignalingunderlyingSAIDI,particularlyinsubsequentyearsaftertheevent
Anoutlierhandlingmethodneedstobedeveloped
BothBoufordandBoxnWhiskersidentify
outliers,howeverBoufordappearstohandlemostconsistentlyandrationally
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ase Studies1201200
Un der ly in g Maj orEvent CatastrophicEve nt Tme d
1001000
experienced
And the methods
signaled them
differentl
60600
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IEEE JB B&W IEEE B&W JB
U57 U94
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methodsidentifiedtohandlecatastrophicdaysisabletoconsistentlyoperatereasonablyacrossalldatasetstested
TheTaskForcerecommendsthatanadditioninthestandardineitherSection6.3ortheAnnexbeincludedtoinformstandardusers
a outt e ssue
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NewSection6.3in1366
When using daily SAIDI and the 2.5method, there is an assumption that the distribution of the
natural log values will most likely resembles a Gaussian distribution, namely a bell-shaped curve.
-,
events (such as hurricanes or ice storms) that result in unusually sizable daily SAIDI values. The
events that give rise to these particular days, considered catastrophic events, have a low
probability of occurring. However, the extremely large daily SAIDI values may tend to skew the
,
increase in its standard deviation. Large daily SAIDI values, caused by catastrophic events, will
exist in the data set for five years and could cause a relatively minor upward shift in the resulting
reliability metric trends. While significant study was undertaken to develop objective methods for
reliability trend), the methods that were developed, in order to be universally applied, caused, for
many utilities, catastrophic events to occur far too often to accept as being reasonable. In addition,
the elimination of catastrophic events from the calculation of the major event threshold caused, in
,
years. It is recommended that the identification and processing of catastrophic events for reliabilitypurposes should be determined on an individual company basis by regulators and utilities, since no
objective method has been devised that can be applied universally to achieve acceptable results.
17
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ReliabilityWG2010Benchmarkingsurvey
definition
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Twomainreasonsfordifferences:
ExclusionCriteriaDifferences(Basis)
.
addressesdatabasisissuesbyclearlydefiningtherules.
ItDOESNOTaddressthedatacollectionissues ThisisbeingaddressedbyIEEEP1782
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Urban,Suburban,Rural
Suburban>50cust/mi=150cust/mi (93cust/km)
2010Survey
10Urbancompanies 18Suburbancompanies
28Ruralcompanies
19Unclassifiedcompanies
20
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78,634,730customersrepresentedinUS&
Small,Medium,Large ,
Medium>100,000and=1Mcustomers
2010Survey
26Smallcompanies
56Mediumcompanies
27Largecompanies
21
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Morethan200 Companieshaverespondedatsometime
2010Survey
106 uniqueentriesrespondedin2010;109totalentriesin
All Respondents 2010
SAIDI IEEE SAIDI All SAIFI IEEE SAIFI All CAIDI IEEE CAIDI All0 MIN 21.43 42.29 0.42 0.68 20.61 29.02
1 Q1 89.47 124.58 0.92 1.16 87.94 102.08
2 MEDIAN 127.71 211.34 1.17 1.41 106.15 132.51
3 Q3 158.43 346.97 1.46 1.83 122.18 194.33
4 MAX 548.39 1806.34 4.65 5.11 219.92 743.70
22
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Small Respondents 2010
SAIDI IEEE SAIDI All SAIFI IEEE SAIFI All CAIDI IEEE CAIDI All
0 MIN 48.91 48.91 0.65 0.72 61.39 68.33
1 Q1 75.72 116.89 0.92 1.26 77.38 88.83
2 MEDIAN 120.99 162.53 1.24 1.43 97.13 108.793 Q3 161.97 314.64 1.47 1.98 116.57 151.51
4 MAX 548.39 1806.34 4.14 4.73 217.38 743.70
Medium Respondents 2010
SAIDI IEEE SAIDI All SAIFI IEEE SAIFI All CAIDI IEEE CAIDI All
0 MIN 21.43 42.29 0.42 0.68 20.61 29.02
1 Q1 94.85 129.52 1.01 1.15 90.68 108.83. . . . . .
3 Q3 159.62 348.15 1.45 1.83 122.17 198.83
4 MAX 418.40 1564.35 4.65 5.11 199.01 574.41
Large Respondents 2010
0 MIN 53.64 78.23 0.54 0.88 65.84 73.511 Q1 94.27 142.48 0.90 1.09 89.85 121.52
2 MEDIAN 124.13 247.69 1.06 1.35 110.35 167.84
3 Q3 157.73 326.67 1.24 1.62 128.39 237.42
23
. . . . . .
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Region 0: Spans States or Unknown
eg on : or eas
Region 2: Mid-AtlanticMidwest: 27
Participants Northeast: 13
Participants
Region 3: Southeast
Region 4: Midwest
Northwest: 15
Participants
Southwest: 11 Mid-Atlantic:
Region 5: Southwest
Re ion 6: South
South: 10
ParticipantsSoutheast : 6
Participants
Region Color Name # Co
0 Black Spans states 3
Region 7: Northwest
2 Yellow MidAtlantic 243 Green Southeast 6
4 Light Blue Midwest 27
5 Dark Blue Southwest 11
6 Purple South 10
7 Red Northwest 15
24
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SAIDI_IEEE
6
U57
500
600
4th Quartile
U7
6
U1
04
U
119
U127
U134
U1
300
400
inu
tes
il
06398
2456109
826100
133
55
U7U123
U162
U67
U199
U110
U68
U157
U146
U195
U165
U1U29
U129
U95
U63
U90
U23
U14
U171
U88
U111
U120
U194
U49
U20
U191
U87
U83
U80
U10
U192
U38
U54
U65
U169
U128
U44
U168
U17
U9U84
U8U16
U132
U47
U200
U125
U159
U108
U25
U51
U58
U121
U117
U53
U4U15
U52
U130
U160
U131
U167
U19
U40U60U116
U85
U43
U22
U61
U62U12
U106
U94U64
U11
2001st Quartile 2nd Quartile
r ua r ile
U103
U177U46
U1
61
U
197
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137
U
196
U166
U21
U17
U17
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0
U103
U46
U197
U196
U21
U172
U41
U170
U198
U56
U82
U100
U55
U123
U67
U110
U157
U195
U1
U129
U63
U23
U171
U111
U194
U20
U87
U80
U192
U54
U169
U44
U17
U84
U16
U47
U125
U108
U51
U121
U53
U15
U130
U131
U19
U60
U85
U22
U62
U106
U64
U76
U119
U134
U57
Utility
25
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SAIFI_IEEE
U57
U157
4
4.5
5
4th Quartile
142
U126
U104
U76
U64
2.5
3
3.5
Inte
rruptions
2nd Quartile
3rd Quartile
7621U41
U166
U162
U172
U164
U197
U15
U173
U82
U137
U146
U128
U29
U56
U55
U49
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U63
U103
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U161
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U7U196
U100
U68
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U20
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U8U94
U171
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U116
U61
U134
U119
U22
U12
U130
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U62U1U
1
1.5
21st Quartile
U17UU
0
0.5
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U21
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U65
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U22
U12
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U62
U114
U52
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U104
U76
U64
U57
U157
Utility
26
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CAIDI_IEEE
3126
U119
U127U106U134
U15
200
4th Quartile
0 954666 30181
91
68129
55
U41
U199
U67
U111
U17
U10
U82
U120
U125
U200
U44U11
7
U121
U8
3
U2
3
U8
8
U54
U132
U
9
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21
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76
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62
U
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U
168
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U58
U160
U25
U131
U16
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U114
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U116
U20
U60
U61
U80
U12U63
U29
U40
U51
U162
U57
U85
U19
U194
U4U53
U94
U49U169
U38
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U47U128U4U
150
Minutes
2nd Quartile
3rd Quartile
U157
U103
U161U196U123
U163
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U177
U189
U197
U133
U170
U24
U198
U109
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U46
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U52
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50
1st Quartile
0
U157
U103
U161
U196
U123
U163
U137
U177
U189
U197
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U170
U24
U198
U109
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U15
27
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,
includeseverythingcustomersexperienced
handlemajorevents.
Thefollowingslidesshowdatawithout
segmentat on.
28
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SAIDI_ALL
U114
U134
1600
1800
2000
4th Quartile
U117
U76
U57U159
1000
1200
1400
nut
es
52 03047U
108
U119
U85
U38
U8U194
U55
U116
U104
U137
U131U65
U16
U43
U15U106
U87
U25U49U22
U127
U53
U126
U128U129
400
600
800Mi
2nd Quartile
3rd Quartile
U161
U103
U197
U170
U196
U177
U109
U41
U82
U162
U68
U100
U198
U173
U163
U95
U63
U90
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U67
U54
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U21
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U83
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7
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U120
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71
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3
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166
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60
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46
U
52
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U1U1U9U4
0
200
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U82
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U21
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U9
U23
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U60
U52
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U12
U61
U7
U62
U132
U17
U160
U94
U108
U85
U8
U55
U104
U131
U16
U15
U87
U49
U127
U126
U129
U76
U159
U134
1st Quartile
29
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SAIFI_ALL
U57
U157
5
6
4th Quartile
117
U126
U64U
104U114
U76
3
4
erruptions
2nd Quartile
3rd Quartile
271772170
6321
U46
U169
U161
U29
U68
U15
U189
U20
U146
U164
U109
U6U80
U67
U196
U56
U172
U173
U110
U24
U100
U38
U88
U137
U128
U1U54
U47
U4U133
U95
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U23
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U163
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U103
U7U192
U195
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U165
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U168
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U16
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U119
U194
U84
U22
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U127
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U65U200
U166
U134
U52U
2
Int
1st Quartile
U16
U19
U4U1UU
0
U162
U197
U41
U177
U82
U170
U63
U21
U46
U169
U161
U29
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U189
U20
U146
U164
U109U6
U80
U67
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U56
U172
U173
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U54
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U58
U106
U168
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U10
U43
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U167
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U60
U129
U14
U40
U19
U53
U49
U123
U132
U17
U160
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U12
U125
U121
U16
U171
U25
U87
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U131
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U84
U22U8
U62
U130
U127
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U65
U200
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U134
U52
U117
U126
U64
U104
U114
U76
U57
U157
Utility
30
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CAIDI_ALL
U159
U134
600
700
800
4th Quartile
8 3137
U49U106U
53U117U15
U129
U114U128
400
500
Minutes
2nd Quartile
3rd Quartile
7361U196
U197
U170
U109
U90
U198
U123
U163
U64
U100
U166
U95
U52
U173
U133
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U111
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U164
U54
U10
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U56
U167
U67
U192
U44
U104
U84
U58
U24
U191
U62
U83
U120
U63
U88
U20
U9U125
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U12
U146
U121
U61
U80
U130
U199
U169
U29
U60
U168
U23
U4U132
U40
U17
U160
U94
U119
U8U19
U21
U194
U7U51
U116
U65
U195
U131
U85U126U16
U108
U76
U47
U46U87
U57
U55
U25
U127
U22U U
100
200
300
1st Quartile
U15
U10U1
0
U157
U103
U161
U196
U197
U170
U109
U90
U198
U123
U163
U64
U100
U166
U95
U52
U173
U133
U177
U68
U165
U111U1
U200
U110
U171
U14
U82
U41U6
U172
U164
U54
U10
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U44
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U58
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U62
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U120
U63
U88
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U125
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U61
U80
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U60
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U119U8
U19
U21
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U51
U116
U65
U195
U131
U85
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U16
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U76
U47
U46
U87
U57
U55
U25
U127
U22
U38
U43
U137
U49
U106
U53
U117
U15
U129
U114
U128
U159
U134
31
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2010IEEESurvey TrendsforSAIDIbyQuartiles
2005 to 2010 IEEE SAIDI Benchmarking Quartiles
410
497
375400425450
475500
Max
1884
Max548
Max502
386
250
275300325350
Max SAIDI 4th Qtrle SAIDI
3rd Qtr le SAIDI 2nd Qtr le SAIDI
Min SAIDI NA Average SAIDI
192 198 200200 167 158
145 146144
162116 128
98 105 109 12481
89100125150175200
Minutes
17 20 25 20 15 2102550
2005 2006 2007 2008 2009 2010
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2010IEEESurvey TrendsforSAIFIbyQuartiles
2005 to 2010 IEEE SAIFI Benchmarking Quartiles
4.464.65
4.0
4.5
5.0Max SAIFI 4th Qtrle SAIFI
3rd Qtr le SAIFI 2nd Qtr le SAIFI
Min SAIFI NA Average SAIFI
2.98 3.203.12
2.5
3.0
3.5
1.63 1.70 1.711.60 1.49
1.461.39 1.361.33 1.35 1.12 1.17
1.09 1.11 1.06 1.12 0.93
1.0
1.5
2.0
Events
.
0.36 0.63 0.37 0.32 0.39 0.42
0.0
0.5
2005 2006 2007 2008 2009 2010
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2010IEEESurvey TrendsforCAIDIbyQuartiles
2005 to 2010 IEEE CAIDI Benchmarking Quartiles
224 220225
250
275 Max CAIDI 4th Qtrle CAIDI Line
3rd Qtrle CAIDI Line 2nd Qtrle CAIDI Line
Min CAIDI NA Average CAIDI
Max 1059
206 212
127 131 127 139 121 122
150
175
200
s
105 108109 117 102 106
83 8285
9983
88
50
75
100
Minute
20 19 30 25 20 210
25
2005 2006 2007 2008 2009 2010