2012Apr TDM McDaniel

8/12/2019 2012Apr TDM McDaniel

1/34

1

UsesofIEEE1366and

CatastrophicDays

JohnMcDaniel

ViceChair DistributionReliabilityWG

LeadSeniorEngineer NationalGrid


2/34

GoodbaselineforCatastrophicDaydiscussion


3/34

.

Aclearerviewofperformance,bothona

a y as san

DuringMajorEvents Canformasolidbasisforreviewof

operationaleffectiveness,decisionmaking

andpolicymaking.

Moreconsistentbenchmarking.


4/34

Definitionmustbeunderstandablebyallandeasyto

apply.

Definition must be s ecific and calculated usin thesameprocessforallutilities.

. Largeandsmall,urbanandrural.

SAIDIwaschosenastheindicator

becauseitissizeindependentand itisthebestindicatorofsystemstressesbeyondthose

thatutilitysstaff,buildanddesigntominimize.


5/34

The2.5BetaMethodologyallowssegmentation

.

Onesetrepresentsthoseeventsofsuchareliability

toadequatelyrespond.(majorevents).

thoseeventsthatacompanyhasbuiltthesystemto

withstandandstaffedtores ondtoinamannerthat

doesnotrequireacrisismodeofoperation.(dayto

dayoperation).


6/34

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.


7/34

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


8/34

.

willallowforconsistentcalculationofreliability

,

providecompaniesandcommissionswithamore

accurate indication of a Com an s controllable

servicequalityresults,

allowaclearreviewofcom an res onsetocrisis

modeevents,and

providealessdistortedindicationofthereliability

resultsforcompaniesofallsizes.


9/34

applicationofIEEEStd.1366foundthat

changesintmedwhichoftenimpactthenext5

DistributionReliabilityWGformedaTask


10/34

significantoutlierscouldimprovethemajoreventthresholdcalculationprocess

Severalmethodswereproposed

BetaVariables(Twooriginallycontemplated) BoxandWhiskers


11/34

outlierdaystoensurethatsubsequent

performance,andisnottaintedbythe


12/34

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


13/34

Evaluatetheimpactofthesedays,onSAIDI,

Identifycompanieswithnocatastrophicdays

beingevaluated

, me , .foreachcompanyrepresented


14/34

dataandholdthepotentialfornotproperlysignalingunderlyingSAIDI,particularlyinsubsequentyearsaftertheevent

Anoutlierhandlingmethodneedstobedeveloped

BothBoufordandBoxnWhiskersidentify

outliers,howeverBoufordappearstohandlemostconsistentlyandrationally


15/34

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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00

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IEEE JB B&W IEEE B&W JB

U57 U94


16/34

methodsidentifiedtohandlecatastrophicdaysisabletoconsistentlyoperatereasonablyacrossalldatasetstested

TheTaskForcerecommendsthatanadditioninthestandardineitherSection6.3ortheAnnexbeincludedtoinformstandardusers

a outt e ssue


17/34

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


18/34

ReliabilityWG2010Benchmarkingsurvey

definition


19/34

Twomainreasonsfordifferences:

ExclusionCriteriaDifferences(Basis)

.

addressesdatabasisissuesbyclearlydefiningtherules.

ItDOESNOTaddressthedatacollectionissues ThisisbeingaddressedbyIEEEP1782


20/34

Urban,Suburban,Rural

Suburban>50cust/mi=150cust/mi (93cust/km)

2010Survey

10Urbancompanies 18Suburbancompanies

28Ruralcompanies

19Unclassifiedcompanies

20


21/34

78,634,730customersrepresentedinUS&

Small,Medium,Large ,

Medium>100,000and=1Mcustomers

2010Survey

26Smallcompanies

56Mediumcompanies

27Largecompanies

21


22/34

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


23/34

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

. . . . . .


24/34

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


25/34

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

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U67

U199

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U68

U157

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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

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U62U12

U106

U94U64

U11

2001st Quartile 2nd Quartile

r ua r ile

U103

U177U46

U1

61

U

197

U

137

U

196

U166

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0

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U20

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U54

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U44

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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

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1

1.5

21st Quartile

U17UU

0

0.5

U177

U46

U21

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U162

U172

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U15

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U76

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Utility

26


27/34

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

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U162

U57

U85

U19

U194

U4U53

U94

U49U169

U38

U108

U47U128U4U

150

Minutes

2nd Quartile

3rd Quartile

U157

U103

U161U196U123

U163

U137

U177

U189

U197

U133

U170

U24

U198

U109

U90

U46

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U100

U52

U64

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U14

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U6U7U1U8U1U5U1UUUU

50

1st Quartile

0

U157

U103

U161

U196

U123

U163

U137

U177

U189

U197

U133

U170

U24

U198

U109

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U15

27


28/34

,

includeseverythingcustomersexperienced

handlemajorevents.

Thefollowingslidesshowdatawithout

segmentat on.

28


29/34

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

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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

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U111

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U14

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U17

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0

200

U161

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U85

U8

U55

U104

U131

U16

U15

U87

U49

U127

U126

U129

U76

U159

U134

1st Quartile

29


30/34

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

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U110

U24

U100

U38

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U1U54

U47

U4U133

U95

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U23

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U83

U163

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U103

U7U192

U195

U55

U165

U58

U106

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U108

U10

U43

U90

U167

U120

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U51

U9U60

U129

U14

U40

U19

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U17

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U61

U12

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U16

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U25

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U119

U194

U84

U22

U8U62U130

U127

U159

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

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U164

U109U6

U80

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U56

U172

U173

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U24

U100

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U54

U47U4

U133

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U83

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U103U7

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U55

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U106

U168

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U10

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U120

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U60

U129

U14

U40

U19

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U49

U123

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U17

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U44

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U85

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U12

U125

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U16

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U25

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U84

U22U8

U62

U130

U127

U159

U65

U200

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U52

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U64

U104

U114

U76

U57

U157

Utility

30


31/34

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

U177

U68

U165

U111

U1U200

U110

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U14

U82

U41

U6U172

U164

U54

U10

U189

U56

U167

U67

U192

U44

U104

U84

U58

U24

U191

U62

U83

U120

U63

U88

U20

U9U125

U162

U12

U146

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U61

U80

U130

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U169

U29

U60

U168

U23

U4U132

U40

U17

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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

U189

U56

U167

U67

U192

U44

U104

U84

U58

U24

U191

U62

U83

U120

U63

U88

U20U9

U125

U162

U12

U146

U121

U61

U80

U130

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U29

U60

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U23U4

U132

U40

U17

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U94

U119U8

U19

U21

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U51

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U65

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U76

U47

U46

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U57

U55

U25

U127

U22

U38

U43

U137

U49

U106

U53

U117

U15

U129

U114

U128

U159

U134

31


32/34

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


33/34

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

2012Apr TDM McDaniel

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