5.3 ERMUSR 03-13-2017 Elk River
Municipal Utilities UTILITIES COMMISSION MEETING
TO: FROM:
Elk River Municipal Utilities Commission Mark Fuchs—Electric Superintendent
John Dietz—Chair
Al Nadeau—Vice Chair
Daryl Thompson—Trustee
MEETING DATE: AGENDA ITEM NUMBER:
March 13, 2017 5.3
SUBJECT:
2016 Electric Reliability Report
BACKGROUND:
Minnesota Rules Chapter 7826 Public Utilities Commission Electric Utility Standards cover
safety, reliability, service, and reporting requirements. Per 7826.0100(A), municipal utilities are
exempt from these requirements. However, the Elk River Municipal Utilities Commission
adopted a number of parts of this chapter as a Distribution Reliability Standard policy requiring
annual reporting on system reliability.
DISCUSSION:
In 2016, our reliability index numbers remain excellent. These reliability index numbers reflect
the condition of our robust electrical system as well as the superb response time from our local
line crews. This also reflects the local accountability, long term visioning on system design, and
ongoing system maintenance. The table below reflects the number of customer outage minutes
from 2006 through 2016.
Year #of Customers #of Outage Minutes #of Outages
2006 8804 314,510 73
2007 9163 67,845 47
2008 9067 155,768 57
2009 9170 46,706 49
2010 9207 526,144 77
2011 9576 297,359 51
2012 9285 71,496 57
2013 9285 55,451 31
2014 9426 243,965 44
2015 9449 225,337 41
2016 10862 432,310 59
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A number of reliability indexes are used in the electric industry to make it easier to compare
performance of electric utilities. Listed below are the reliability indexes along with ERMU's
2015 and 2016 statistics. Also attached is the American Public Power Association's (APPA)
eReliability Annual Report which contains published averages that can be used by utilities to
better understand the performance of their electric system relative to other utilities nationally and
to those within their region or size class.
Average Service Availability Index (ASAI)
ASAI is a measure of the average availability of the sub-transmission and distribution systems to
serve customers. It is the ratio of the total customer minutes that service was available to the total
customer minutes available in a time period. This is normally expressed as a percentage.
ERMU's 2016 ASAI is 99.9922%Availability ERMU's 2015 ASAI is 99.9943%Availability
Customer Average Interruption Duration Index (CAIDI)
CAIDI is defined as the average duration (in minutes) of an interruption experienced by customers
during a specific time frame. It is calculated by summing the customer minutes off during each
interruption in the time period and dividing this by the number of customers experiencing one or more
sustained interruptions during the time period. The resulting unit is minutes. The index enables utilities to
report the average duration of a customer outage for those customers affected.
ERMU's 2016 CAIDI is 150.4193 minutes ERMU's 2015 CAIDI is 71.157 minutes
System Average Interruption Duration Index (SAID!)
SAID! is defined as the average interruption duration for customers served during a specified time
period. It is determined by summing the customer minutes off for each interruption during a specified
time period and dividing that sum by the average number of customers served during that period. The
unit is minutes. This index enables the utility to report how many minutes' customers would have been
out of service if all customers were out at one time.
ERMU's 2016 SAIDI is 41.0234 minutes ERMU's 2015 SAIDI was 23.723 minutes
System Average Interruption Frequency Index (SAIFI)
SAIFI is defined as the average number of times that a customer is interrupted during a specified
time period. It is determined by dividing the total number of customers interrupted in a time period by the
average number of customers served. The resulting unit is "interruptions per customer."
ERMU's 2016 SAIFI is 0.2727 ERMU's 2015 SAIFI was 0.333
El
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Staff reviews these statistics in detail to find ways to continually improve our system. These
statistics are very good compared to other utilities. As indicated by the following pie chart and
statistics list, you can see the cause of the outages that we experienced in 2016.
Outage Cause Count View
Tree 7 (%
tarter Siqnd Equipment 4 4
iill
Vehicke Accident 4
Human Accident 3
1,:.
lightning 3 0
qy, MSS n 7
5% ,' 'flashover "" 1
.,. -,.m Cut 1 1,
Huaren Acg' s" <
burg False
d 1 � 14% ire Department 1
.- . ... Weather 1
11111
+„ byq� rrt.; llect StraCe 1 IR
Unscheduled 1
Equmment Tree
7% 12% WyaerSuppy 1 1)
vegetatlan <>
Tva Of
Many investor owned and cooperative run utilities "storm normalize"their reliability numbers
by removing"major outages" due to storms from their reliability index number calculations.
Our reliability numbers have not been storm normalized. Our numbers reflect these major
outages and represent what our customer actually experienced in 2016. Overall, our electric
distribution system is robust and our reliability is very high.
ACTION REQUESTED:
Staff requests the Commission receive and file the APPA eReliability Tracker 2016 Annual
Report for Elk River Municipal Utilities.
ATTACHMENT:
• APPA eReliability Tracker 2016 Annual Repor for Elk River Municipal Utilities
IIE l'_ _
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eReliabil'tlY acker .
•
2016 Annual Report
Elk River Municipal Utilities
The eReliability Tracker Annual Report was created by the American Public Power Association to assist
utilities in their efforts to understand and analyze their electric system. This report focuses on distribution
system reliability across the country and is customized to each utility. The data used to generate this
report reflect activity in the eReliability Tracker from January 1, 2016 to December 31, 2016. Note that if
you currently do not have a full year of data in the system, this analysis may not properly reflect your
utility's statistics since it only includes data recorded as of February 13, 2017; therefore, any changes
made after that date are not represented herein.
I. General Overview
Reliability reflects both historic and ongoing engineering investment decisions within a utility. Proper use
of reliability metrics ensures that a utility is not only performing its intended function, but also is providing
service in a consistent and effective manner. Even though the primary use of reliability statistics is for self-
evaluation, utilities can use these statistics to compare with data from similar utilities. However,
differences such as electrical network configuration, ambient environment, weather conditions, and
number of customers served typically limit most utility-to-utility comparisons. Due to the diverse range of
utilities that use the eReliability Tracker, this report endeavors to group utilities by size and region to
improve comparative analyses while reducing differences.
Since this report contains overall data for all utilities that use the eReliability Tracker, it is important to
consider the effect that a particularly large or small utility can have on the rest of the data. To ease the
issues associated with comparability, reliability statistics are calculated for each utility with their respective
customer weight taken into account prior to being aggregated with other utilities. This means that all
utilities are equally weighted and all individual statistics are developed on a per customer basis.
The aggregate statistics displayed in this report are calculated from utilities that experienced more than
two outages in 2016. Also, utilities that experienced no outages this year, or did not upload any data, will
have None/Null values in their report for their utility-specific data and were not included in the aggregate
analysis.
The aggregate statistics provided in the following sections of the report are based on data from 200
utilities.
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This report separates utilities into groups of equal numbers of utilities according to their number of
customers served. As seen in Table 1, the customer size distribution of utilities that use the eReliability
Tracker is split into five distinct customer size class groups.
Your utility belongs to customer size class 4 and region 3.
Table 1
Customer size range per customer size class
Class 1: 0-1,517
Class 2: 1,517-3,393
Class 3: 3,393-6,740
Class 4: 6,740- 13,223
Class 5: 13,223- 1,481,735
Since the utilities considered in this report represent a wide variety of locations across the United States,
each utility is also grouped with all others located in their corresponding American Public Power
Association region. Figure 1 shows the number of utilities using the eReliability Tracker in each
Association region and Figure 2 displays the Association's current United States map of regional divisions.
Figure 1
Number of eRT utilities per Association region
140 - 131
(r) 120 -
= 100 - 76
D 80 -
61
60 _ 37
40 52
0 18
U 20 - 13 10 I ME
13 2
1 2 3 4 5 6 7 8 9 10
Association Region
Figure 2
Association Map of Regions as of January 1, 2016
Region 9
Region 3
Region 8
Region 2
Region 7
Region 6
Region 7 Region 5
Region 4
Region to
Aaska America Svowa Puerto Rico
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II. IEEE Statistics
When using reliability metrics, a good place to start is with the industry standard metrics found in the IEEE
1366 guide. For each individual utility, the eReliability Tracker performs IEEE 1366 calculations for
System Average Interruption Duration Index(SAIDI), System Average Interruption Frequency Index
(SAIFI), Customer Average Interruption Duration Index (CAIDI), Momentary Average Interruption
Frequency Index (MAIFI) and Average Service Availability Index (ASAI).
When collecting the necessary data for reliability indices, utilities often take differing approaches. Some
utilities prefer to include information as detailed as circuit type or phases impacted, while others include
only the minimum required. In all cases, the more details a utility provides, the more practical their
analysis will be. As a basis for calculating these statistics in the eReliability Tracker, the following are
required to create an outage in the system:
- Total number of customers served on the day of the outage
- Time and date when the outage began
- Primary cause of outage
-Address where the outage was located
- Number of customers that lost power
It is important to note that the time when the outage ended is not required in case the outage is ongoing;
therefore, outages without end dates at the time of the report analysis are not included in the indices that
measure duration, such as SAIDI and CAIDI. However, they are included in the calculations measuring
interruption frequencies, such as SAIFI or MAIFI, as well as in the analysis of outage causes.
Due to the differences in how some utilities analyze major events (MEs) relative to their base statistics, it
is important to note how they are calculated and used in this report. An example of a major event could be
severe weather, such as a tornado or thunderstorm, which can lead to unusually long outages in
comparison to your distribution system's typical outage. In the eReliability Tracker and in this report, the
Association's major event threshold is used, which is a calculation based directly on outage events, rather
than event days. The major event threshold allows a utility to remove outages that exceed the IEEE 2.5
beta threshold for events, which takes into account the utility's past outage history up to 10 years. In the
eReliability Tracker, if a utility does not have at least 36 outage events prior to the year being analyzed, no
threshold is calculated; therefore, the field below showing your utility's threshold will be blank and the
calculations without MEs in the SAIDI section of this report will be the same as the calculations with MEs
for your utility. More outage history will provide a better threshold for your utility.
Your utility's APPA major event threshold is 7.3199 (minutes)!-
If
minutes)1If you wish to remove major events, the threshold calculated above is important to note because it impacts
your SAIDI analysis. For the next year, based on your utility's outage history, any event with a SAIDI
greater than 7.3199 minutes is considered to be a major event and can be removed in your
analysis.
The tables in this section can be used by utilities to better understand the performance of their electric
system relative to other utilities nationally and to those within their region or size class. In the SAIDI
section, indices are calculated for all outages with and without major events; furthermore, the data are
broken down to show calculations for scheduled and unscheduled outages. For each of the reliability
indices, the second table breaks down the national data into quartile ranges, a minimum value, and a
maximum value.
1 If there is no major event threshold calculated for your utility,these fields are left blank and the calculations in this report including Major Events and excluding
them will be the same.Your utility must have at least 36 outage events recorded in the eReliability Tracker in order to calculate a Major Event Threshold.
3
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System Average Interruption Duration Index (SAIDI)
SAIDI is defined as the average interruption duration (in minutes) for customers served by the utility
system during a specific time period.
Since SAIDI is a sustained interruption index, only outages lasting longer than five minutes are included in
the calculations. SAIDI is calculated by dividing the sum of all customer interruption durations within the
specified time frame by the average number of customers served during that period. For example, a utility
with 100 customer minutes of outages and 100 customers would have a SAIDI of 1.
Note that in the tables below, scheduled and unscheduled calculations include major events. Also note
that wherever major events are excluded, the exclusion is based on the APPA major event threshold.
Table 2
Average SAIDI for all utilities that use the eReliability Tracker(with and without MEs), belong to
your region, and are grouped in your customer size class
All No MEs Unscheduled Scheduled
Your utility's SAIDI: 41.0234 l 20.4977 40.9308 0.0926
Average eReliability Tracker SAIDI 92.3956 50.1741 87.6458 4.7532
Average SAIDI for Utilities Within Your Region 88.3391 36.6915 77.3377 10.9742
(Average SAIDI for Utilities Within Your Customer Size Class 85.4668 27.9804 83.2979 2.2003
Table 3
Summary statistics of the SAIDI data compiled from the eReliability Tracker
All No MEs Unscheduled Scheduled
Minimum Value 0.2845 0.2845 1 0.2845 0
First Quartile (25th percentile) 20.3321 10.2855 17.6174 0
Median Quartile (50th percentile) 51.9255 23.819 48.841 0.0486
Third Quartile(75th percentile) 108.162 49.0237 94.7276 0.887
Maximum Value 828.65 586.0228 828.6135 325.8937
Figure 3
Average SAIDI for all utilities that use the eReliability Tracker per region
160 - 145.1569 151.0079
140 - 126.197
c 120 - 107.9024
E 100 - 88.3391
E
80 - 66.7488 68.8783
o)
Elili
- 49.3604
EL'
39
> -
1 2 3 4 5 6 7 8 9
Association Regions
4
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System Average Interruption Frequency Index (SAIFI)
SAIFI is defined as the average number of instances a customer on the utility system will experience an
interruption during a specific time period.
Since SAIFI is a sustained interruption index, only outages lasting longer than five minutes are included in
the calculations. SAIFI is calculated by dividing the total number of customer interruptions by the average
number of customers served during that time period. For example, a utility with 150 customer interruptions
and 200 customers would have a SAIFI of .75 interruptions per customer.
Table 4
Average SAIFI for all utilities that use the eReliability Tracker, belong to your region, and are
grouped in your customer size class
Your utility's SAIFI: 0.2727
Average eReliability Tracker SAIFI 0.7961
(Average SAIFI for Utilities Within Your Region 0.631
!Average SAIFI for Utilities Within Your Customer Size Class 0.7013
Table 5
Summary statistics of the SAIFI data compiled from the eReliability Tracker
Minimum Value 0.0049
First Quartile (25th percentile) 0.254
Median Quartile(50th percentile) 0.5557
Third Quartile(75th percentile) 1.1002
Maximum Value 4.7797
Figure 4
Average SAIFI for all utilities that use the eReliability Tracker per region
1.2 — 1.1093 1.1345
c
1.0424
° 1 -
Q
0.8 -
0.6907 0.631 0.6356 0.6521
0.6 0.5664 0.5363
Q
0.4 -
a)co
0.2 -
Q
0
1 2 3 4 5 6 7 8 9
Association Regions
5
98
Customer Average Interruption Duration Index (CAIDI)
CAIDI is defined as the average duration (in minutes) of an interruption experienced by customers during
a specific time frame.
Since CAIDI is a sustained interruption index, only outages lasting longer than five minutes are included in
the calculations. It is calculated by dividing the sum of all customer interruption durations during that time
period by the number of customers that experienced one or more interruptions during that time period.
This metric reflects the average customer experience (minutes of duration) during an outage.
Table 6
Average CAIDI for all utilities that use the eReliability Tracker, belong to your region, and are
grouped in your customer size class
Your utility's CAIDI: 150.4193
Average eReliability Tracker CAIDI 134.3941
Average CAIDI for Utilities Within Your Region 153.2071
;Average CAIDI for Utilities Within Your Customer Size Class 116.6985
Table 7
Summary statistics of the CAIDI data compiled from the eReliability Tracker
Minimum Value 10.9227
First Quartile (25th percentile) 68.0214
Median Quartile (50th percentile) 93.2064
Third Quartile(75th percentile) 138.9237
Maximum Value 3140.1425
Figure 5
Average CAIDI for all utilities that use the eReliability Tracker per region
600 -
508.2063
500 -
a)
•E 400 -
Q 300 -
U
cn 200 - 153.2071
106.9719 111.1964 119.0704 103.9643
94.0246 93.43 94.958
100 . . ■ . . 111 ■
0
1 2 3 4 5 6 7 8 9
Association Regions
6
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Momentary Average Interruption Frequency Index (MAIFI)
MAIFI is defined as the average number of times a customer on the utility system will experience a
momentary interruption.
In this report, an outage with a duration of less than five minutes is classfied as momentary. The index is
calculated by dividing the total number of momentary customer interruptions by the total number of
customers served by the utility. Momentary outages can be more difficult to track and many smaller
utilities may not have the technology to do so; therefore, some utilities may have a MAIFI of zero.
Table 8
Average MAIFI for all utilities that use the eReliability Tracker, belong to your region, and are
grouped in your customer size class
Your utility's MAIFI: 0.0001
Average eReliability Tracker MAIFI 0.376
Average MAIFI for Utilities Within Your Region 0.5327
--I
Average MAIFI for Utilities Within Your Customer Size Class 0.44
Table 9
Summary statistics of the MAIFI data compiled from the eReliability Tracker
Minimum Value 0
First Quartile (25th percentile) 0
Median Quartile (50th percentile) 0
Third Quartile(75th percentile) 0.0566
Maximum Value 11.1364
Figure 6
Average MAIFI for all utilities that use the eReliability Tracker per region
0.6 - 0.5327 0.5579
0.5204
0.5 - 0.4387 0.429
.I :.: '
0.2307 0.202
2 0.2 -
0.1 0.0705
' 0.0093 11111
1 2 3 4 5 6 7 8 9
Association Regions
7
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Average Service Availability Index (ASAI)
ASAI is defined as a measure of the average availability of the sub-transmission and distribution systems
that serve customers.
This load-based index represents the percentage availability of electric service to customers within the
time period analyzed. It is caclulated by dividing the total hours service is available to customers by the
total hours that service is demanded by the customers. For example, an ASAI of 99.99% means that
electric service was available for 99.99% of the time during the given time period.
Table 10
Average ASAI for all utilities that use the eReliability Tracker, belong to your region, and are
grouped in your customer size class
Your utility's ASAI (%): 99.9922
Average eReliability Tracker ASAI 99.9829
Average ASAI for Utilities Within Your Region 99.9839
Average ASAI for Utilities Within Your Customer Size Class 99.9838
Table 11
Summary statistics of the ASAI data compiled from the eReliability Tracker
Minimum Value 99.8427
First Quartile(25th percentile) 99.9814
1Median Quartile (50th percentile) 99.9902
(Third Quartile(75th percentile) 99.9961
Maximum Value 99.9999
Figure 7
Average ASAI for all utilities that use the eReliability Tracker per region
99.995 - 99.993
99.9906
99.99 - 99.9873 99.9869
99.985 - 99.9839
Q 99.98
Q 99.98 - 99.976 99.9766
99.975 - 99.9724
Q' 99.97 -
99.965 -
99.96 ,
1 2 3 4 5 6 7 8 9
Association Regions
8
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2015 Energy Information Administration (EIA) Form 861 Data
Form EIA-861 collects information on the status of electric power industry participants involved in the
generation, transmission, distribution, and sale of electric energy in the United States, its territories, and
Puerto Rico.
EIA surveys electric power utilities annually through EIA Form 861 to collect electric industry data and
subsequently make that data available to the public. In 2014, EIA began publishing reliability statistics in
their survey from utility participants; therefore, the Association included EIA reliability statistics in this
report for informational purposes. Please note that the following data includes investor-owned, rural
cooperative, and public power utilities that were large enough to be required to fill out the full EIA 861, not
the EIA 861-S form (for smaller entities). In addition, since the collection and release of EIA form data lags
by more than a year, the data provided is based on 2015 data only. Therefore, it is suggested that the
aggregate statistics contained herein be used only as an informational tool for further comparison of
reliability statistics.
In the table, if an entity calculates SAIDI, SAIFI, and determines major event days in accordance with the
IEEE 1366-2003 or IEEE 1366-2012 standard, they are included under the "IEEE Method" columns. If the
entity calculates these values via another method, they are included under the "Other Method" columns.
In 2015, there were 1045 utilities that submitted reliability data to the EIA. Additionally, it looks as though a
number of utilities submitted incorrect data, which shows itself most in the average SAIFI numbers. For
more general information on reliability metrics you can see the Association's website at
http://publicpower.org/reliability. Although EIA collected other reliability-related data, the tables below only
include SAIDI and SAIFI data. The full set of data can be downloaded at this link:
http://www.eia.gov/electricity/data/eia861/
Table 12
Summary statistics of the SAIDI data compiled from 2015 data collected by EIA
IEEE Method Other Method
All No MEDs All No MEDs
Average 243.7241 131.9638 202.4763 114.4136
Minimum Value 0.0000 0.0000 0.0000 0.0000
First Quartile (25th percentile) 69.7200 52.5788 21.0500 16.1773
Median Quartile(50th percentile) 145.6100 100.0755 79.8900 50.1850
Third Quartile(75th percentile) 263.2730 162.9023 185.3515 127.5500
Maximum Value 7662.6000 1465.4000 9076.3230 3011.4170
Table 13
Summary statistics of the SAIFI data compiled from 2015 data collected by EIA
IEEE Method Other Method
All No MEDs All I No MEDs
Average 1.9551 1.5185 2.8715 1.6314
Minimum Value 0.0000 0.0000 0.0000 0.0000
First Quartile(25th percentile) 0.7855 0.6880 0.4000 0.3340
Median Quartile(50th percentile) 1.3300 1.0605 0.9600 0.8000
Third Quartile(75th percentile) 2.0050 1.5700 ; 1.7500 1.4745
Maximum Value 159.5570 82.0730 ;1 215.9600 64.6800
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III. Outage Causes
Equipment failure, extreme weather events, wildlife and vegetation are some of the most common causes
of electric system outages. However, certain factors, such as regional weather and animal/vegetation
patterns, can make a different set of causes more prevalent to a specific group of utilities. The following
sections of this report include graphs depicting common causes of outages for your individual utility, all
utilities in your region, and all utilities using the eReliability Tracker. The charts containing aggregate
information are customer-weighted to account for differences in utility size for a better analytical
comparison. For example,a particularly large utility may have a large number of outages compared to a
small utility; in order to avoid skewing the data towards large utilities, the number of cause occurrences is
divided by customer size to account for the differences. In the figures below, the data represent the
number of occurrences for each group of 1000 customers. For instance, a customer-weighted occurrence
rate of"1" means 1 outage of that outage cause per 1000 customers on average in 2016.
Note that the sustained outage cause analysis is more comprehensive than the momentary outage cause
analysis due to a bigger and more robust sample size for sustained outages. Regardless, tracking both
sustained and momentary outages helps utilities understand and reduce outages. To successfully use the
outage information tracked by your utility, it is imperative to classify and record outages in detail. The
more information provided per outage, the more conclusive and practical your analyses will be.
Sustained Outage Causes
In general, sustained outages are the most commonly tracked outage type. In many analyses of sustained
outages, utilities tend to exclude scheduled outages, partial power, customer-related problems, and
qualifying major events from their reliability indices calculations. While this is a valid method for reporting,
these outages should be included for internal review to make utility-level decisions. In this section, we
evaluate common causes of sustained outages for your utility, corresponding region, and for all utilities
that use the eReliability Tracker. It is important to note that in this report, sustained outages are classified
as outages that last longer than five minutes, as defined by IEEE 1366.
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Figure 8
Top five customer-weighted occurrence rates for common causes of sustained outages for all
utilities that use the eReliability Tracker Service 2
2 - 1.838
1.5 -
1.0467 1.0382 1.0273
S2 1 0.7041
C 0.5 -
O
0
Tree Equipment Squirrel Electrical Failure Unknown
Outage Causes Types
Figure 9
Top five customer-weighted causes of sustained outages for your utility
1.6 - 1.4234
u)
1.4 -
' 1.2 -
as
a)
1 -
c 0.8 - 0.6642 0.6642
j 0.6 - 0.3795 0.3795
X0.4 -
00.2 -
0
Squirrel Electrical Failure Tree Vehicle Accident Equipment
Outage Cause Types
Figure 10
Top five customer-weighted occurrence rates for sustained outage causes in your region 2
1.2 1.0317
CI) 1
0.7961
o 0.8 0.7574 0.6828 0.647
0.6
a)
'5 0.4
U
O 0.2
0
Squirrel Utility Maintenance Equipment Tree Weather
and Repairs
Outage Cause Types
2
For each utility,the number of occurrences for each cause is divided by that utility's customer size(in 1000s)to create an occurence rate that can be compared
across different utility sizes.
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Momentary Outage Causes
The ability to track momentary outages can be difficult or unavailable on some systems, but due to the
hazard they pose for electronic equipment, it is important to track and analyze momentary causes. In this
section, we evaluate common causes of momentary outages for your utility, region and customer size
class as well as common causes for all utilities that use the eReliability Tracker. Please note that only
outages lasting less than five minutes are classified as momentary, as defined by IEEE 1366.
Figure 11
Top five customer-weighted occurrence rates for common causes of momentary outages for all
utilities that use the eReliability Tracker Service 2
0.18 - 0.1559
0.16 -
a 0.14
(13 0.12 -
cc
• 0.1 -
m 0.08 - 0.0618 0.0575
• 0.06 - 0.0477 0.0464
0.04 -
0.02 -
0 I I I
Unknown Customer Service Tree Storm Utility Maintenance and
Repairs
Outage Cause Types
Figure 12
Top five customer-weighted causes of momentary outages for your utility2'3
0.1 - 0.0948
0.08 -
m
a)• 0.06 -
U
C
N
0.04 -
U
U
O 0.02 -
0 0 0 0
0
Electrical Failure None None None None
Outage Cause Types
3
If your utility has less than eight momentary outages recorded in the eReliability Tracker,this graph will be blank.
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Figure 13
Top five customer-weighted occurrence rates for momentary outage causes in your region
0.25 -
0.1968
a 0.2 - 0.1729
co
0.15 - 0.1222
0.0954
I
0.0686
0.05 -
0
Utility Maintenance Unknown Non-Payment Unknown/Other Storm
and Repairs
Outage Cause Types
Thank you for using the eReliability Tracker, and we hope this report is useful to your utility in analyzing
your system. If you have any questions regarding the material provided in this report, please contact:
APPA's Reliability Team
Michael J. Hyland
Alex Hofmann
Tanzina Islam
Christina Ospina
Ethan Epstein
American Public Power Association
2451 Crystal Drive, Suite 1000
Arlington, VA 22202
reliability@publicpower.org
AMERICAN
PUBLIC The eReliability Tracker was funded by a grant from the Demonstration of
�`.�► Energy & Efficiency Developments (DEED) Program.
Popossorn
� ihimM
ASSOCIATION Copyright 2016 by the American Public Power Association. All rights reserved.
Powering Strong Communities
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