epri 2016 outage data survey - ieeegrouper.ieee.org/groups/td/dist/sd/doc/2016-09-01 epri 2016...
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© 2016 Electric Power Research Institute, Inc. All rights reserved.
Sal MartinoEPRI - Technical Leader
IEEE PES GM 2016
July 19, 2016Boston, MA
EPRI
2016 Outage Data for
Reliability and
Resiliency Survey
2© 2016 Electric Power Research Institute, Inc. All rights reserved.
The Big PictureReliability abroad
Source: G. Rouse and J.K. Galvin, "Electricity Reliability: Problems, Progress, and Policy Solutions,” Galvin Electricity Initiative, 2011
Source: CEER Benchmarking Report 5.2, 2015
2013 Average SAIDI: 208.5Source: 2013 US EIA Survey
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Distribution Grid Resiliency ProjectObjectives
Analyze data provided
Correlate to performance
Projections based on
strategies
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Outage DataLimitations
Inconsistencies
No validation process
Crucial data is missing
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Outage Data Survey
Demographics
Issued in March 2015
13 Questions
22 Respondents
IOU73%
Muni/Co-Op27%
16
6
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Outage Data Survey – Data Collection
Q: “Which set of cause codes are used for internal reporting?”
IEEE 178214%
Custom Cause Codes86%
18
3
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Outage Data Survey – Data Collection
Q: “Who is primarily responsible for entering outage cause?”
Lineworkers54%
Control Center Personnel
27%
Order Dispatchers5%
Engineers9% Other
5%
1 2
12
6
1
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Outage Data Survey – Data Storage
Q: “What is the reliability data ‘system of record’?”
Outage Management System (OMS)
59%
Custom Database/Mainframe
36%
Other5%
8
13
1
9© 2016 Electric Power Research Institute, Inc. All rights reserved.
Outage Data Survey – Data Processing
Q: ”How often do you validate/reconcile data”
38% 38%
14%
10%
Daily Monthly Weekly Other
0%
5%
10%
15%
20%
25%
30%
35%
40%
8 8
3 2
10© 2016 Electric Power Research Institute, Inc. All rights reserved.
Outage Data Survey – Data Processing
Top Items Corrected through Validation
Outage Start and End Times
Number of customers affected
Cause Code
Restoration Steps Consistency
Comments Inconsistent with Data
Entered
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12%
18%
35%
47%
47%
53%
53%
65%
88%
Other
Asset Management System (AMS)
Distribution Management System (DMS)
Vegetation Management System (VMS)
Custom Database/Mainframe
Customer Information System (CIS)
Inspection Records
Geographic Information System (GIS)
Outage Management System (OMS)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Outage Data Survey – Data Analytics
Q: “How do you supplement outage data for analysis?”
15
11
9
9
8
8
6
3
2
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Group Discussion
Topics
Data input GUI
Data Prepopulating
– GPS, AMI, weather, etc.
Outage follow-up practices
Proactive analysis of outage data
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Group Discussion
Lineworker information input
Same interface as office personnel use
Manual Data Entry
Opportunity for improvement with
– Automation
– User Interface
– “Less is More”
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Group Discussion
Q: “Can logic be applied to prepopulate information?”
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Group Discussion
Q: “When is there a need to understand/identify the root
cause of an outage?”
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What information does the
industry collect, and what
information needs to be
collected going forward to
allow us to be more proactive?
Group Discussion
Q: “What outage data can be used proactively?”
17© 2016 Electric Power Research Institute, Inc. All rights reserved.
For more information
Sal Martino
704-595-2832
18© 2016 Electric Power Research Institute, Inc. All rights reserved.
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