GIS Integrated Analytics for
Preventive Maintenance and
Storm Response
Presenter:
John Lauletta, CEO/CTO
Sr. Member, IEEE
Preventive Maintenance
Decision Process
Budget
Circuit Performance
Non Storm-Related Outages on
the Electric Distribution System
Equipment Failure
31%
Miscellaneous
19%
Trees / Vegetation
32%
Animal Contact
18%
Source: U.S. DOE
Preventive Maintenance
Decision Process
Budget
Circuit Performance
Grid Design
Asset Health
Optimized Maintenance
Vegetation Mgmt.
Connectivity
RF Emission Detection
Predictive Maintenance
(PdM)
Predictive Maintenance is based upon
knowing the condition of equipment in
a system.
Predictive Maintenance means using
technologies that tell us what will fail
in the future, not what is failing right
now. Predictions come from
monitoring the condition of
equipment as it is operating.
Here are some ways to measure
equipment condition. 
There are many benefits to conditionsbased maintenance including lowering
cost, improving system performance
and enhancing worker safety. But, how
can the condition of all the equipment
on the grid be measured?
Measuring the Condition of the Grid 
Ultrasonic Emission Detection
Infrared Detection
Visual Detection
Exacter, Inc. Provides: Grid Condition Assessment for Improved
System Resiliency and Reliability
 Based in Columbus, OH
 US Strategic Partners:
 Int’l Alliance Partners
– Australia, Mexico, Canada
 2 US Patents, 7 Int’l Patents
 2 million+ Poles Surveyed
 3rd Party Validation
– U.S. Dept. of Energy (DOE)
– Nat’l Elec. Testing Lab (NETL)
– The Ohio State University
Research Facilities
The Ohio State University High Voltage Laboratory
Initial Research 2004 to 2006
Advanced Research Coninues
Test Fixtures
Surge Arrester Being Studied
Two views of the test setup
Lab Workstation
Faraday Cage
Research Analytics
EXACTER® Sensor
Exacter Acquisition and Analysis Process
Data Acquisition &
Discrimination
Data Analysis
Actionable
Information
Data analyzed for severity,
persistence and prevalence,
enabling:
• Exact locating of failing
component
• Replacement prioritization
RF emissions from arcing
(deteriorated) electrical components
Exacter sensor in vehicle/aircraft collects
the signals and then discriminates and
GPS locates arcing, tracking and leaking
electrical components
Precise GPS coordinates
and relevant conditiondata transmitted
to servers for final
statistical geospatial
analysis
Reports and GIS compatible
information provided to customer
The Need: DOE Smart Grid Project Example
http://www.smartgrid.gov/reports
Condition Assessment:
Select Circuits and Design Survey
Following the selection
of circuits to be
included in the
assessment, Exacter
Data Specialists design
specific survey routes
using public access
roadways. The
EXACTER Sensor is
sensitive in a 200
meter radius from the
vehicle.
Survey Quality Control
Condition Assessment:
Monitor Survey Progress
While the survey is
underway, the path of
the survey vehicle, the
WHITE trace, is
monitored to insure
that the circuits being
assessed are
completely studied.
Accuracy of results is
improved by multiple
passes of the same
route over a four week
period.
Condition Assessment:
Real-time Failure Signature Analysis
Whenever the
EXACTER Sensor
locates a line emission
that correlates to a
Failure Signature a
real-time study is
completed. The 986
RED markers show all
of the studies from the
four-week survey
process.
Condition Assessment:
EXACTER Condition Assessment Results
The 986 RED Failure
Signature Events are
studied by EXACTER
Servers to create this
result: 25 BLUE
Maintenance Groups
where a structure
includes at least one
weakened component.
Analytical Process to Locate
Deteriorated Equipment
Internal Algorithms:
Geographic Circle Calculation
• Calculates coordinates of a
circle which is centered about
a point on the globe
• Difficult cone-sphere
intersection problem
• Adopted a method described
in The Journal of Applied
Meteorology by I. Ruff in 1971
Transmission Equipment Deterioration
Aerial Surveys
Prioritized Maintenance Action:
Select Equipment to Replace
Specific component(s)
that are arcing, leaking
or tracking on those
structures that have
been prioritized for
repair are identified.
Photographs, Maps,
Reports, and GIS Files
are provided.
GIS
.SHP
File
Example: Project Design
• Projects are designed with utility data to create an optimized
price/benefit result
• Utilities:
– Set Goals
– Perform Maintenance
– Measure Results
Outage Causes
Animals and
Other, 37%
Equipment,
31%
Vegetation,
32%
Selected Priority
Feeders to
Assess and
Improve
Affecting
20% of Outages
Predictive
Based
Maintenance
Deferred, Less
Critical , Low
SAIDI Impact
Feeders 11%
Example: Prioritized Worst Performing
Circuit (WPC) Improvement Program
25,000,000.00
Eastern Division
CMI Impact Analysis
Circuit CMI Contribution
5,950 OVHD Miles
35.0%
30.0%
30%
20,000,000.00
26%
25.0%
15,000,000.00
20.0%
17%
73% of Total CMI – 1,904 miles (32%)
10,000,000.00
CMI Result of
Current Programs
9%
8%
5,000,000.00
2%
2%
1%
1
11
21
31
41
51
61
71
81
10.0%
5.0%
5%
-
15.0%
91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251
0.0%
0%
SAIDI
IEEE 1366
Sum of All Customer Interruption Durations
Total Number of Customers Served
71.18
61.33
63.92
23.38
Aggregate Customer Experience
SAIFI
Total # of Customer Interruptions
Total Number of Customers Served
1.52
1.48
1.53
1.03
Aggregate Customer Experience
CAIDI
Sum of All Customer Interruptions
Total Number of Customer Interruptions
Relatively No Change in the Customer Experience
46.73
41.32
41.90
22.78
Aggregate Customer Experience
Non Storm-Related Outages on
the Electric Distribution System
Equipment Failure
31%
Miscellaneous
19%
Trees / Vegetation
32%
Animal Contact
18%
Source: U.S. DOE
Flat Response =
Challenges & Opportunities
500
450
400
CAIDI
350
300
250
200
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
Flat Response =
Challenges & Opportunities
500
450
400
CAIDI
350
300
250
200
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
Target Performance
500
450
400
CAIDI
350
300
250
200
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
Top Decile
20 Years of Design Excellence
500
450
400
CAIDI
350
300
250
200
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
SAIDI Focus
O&M – Workforce Deployment
500
450
400
CAIDI
350
300
250
200
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
SAIFI Focus
Capital Intensive Programs
500
450
400
CAIDI
350
300
250
200
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
Typical Strategies
A
u
t
o
m
a
Tree t
i
o
n
500
450
400
CAIDI
350
300
250
200
60% – 70%
Out of
ROW
Trimming
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
Replace Deteriorated Equipment
A
u
t
o
m
a
Tree t
i
o
n
500
450
400
CAIDI
350
300
250
200
Trimming
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
What is The Strategy to Improve?
How Good Is Good Enough?
•
•
•
•
•
500
SAIDI (CMI)
SAIFI (Number of outages)
CAIDI (CMI)
CEMI (Number of outages)
Targeted Performance:
1st Quartile or Decile
• Stay Ahead of the Bear
450
400
CAIDI
350
300
250
200
150
100
50
0
0
0.5
1
1.5
2
2.5
SAIFI
3
3.5
4
4.5
5
What is Urgent and Important?
Informed Maintenance Decisions
5
4.5
CMI (100,000)
4
3.5
3
2.5
CMI
2
1.5
1
0.5
0
Circuit A
Circuit B
Circuit C
Circuit D
Optimized Selection
5
CMI (100,000)
4.5
4
3.5
3
CMI
2.5
CMI / Mile
2
1.5
1
0.5
0
Circuit A
Circuit B
Circuit C
Circuit D
Circuit Connectivity
Circuit 1
1,000 Customers
CMI1 = CMI2
Circuit 2
120 Customers
Circuit Physical Design
Circuit 1
9 miles of OH
CMI1 = CMI2
1 mile of OH
9 miles of UG
Circuit 2
1 mile of UG
Circuit Critical Connectivity
Circuit 1
100 Customers
CMI1 = CMI2
Circuit 2
100 Customers
Grid Operation Importance
Circuit 1
Smart Grid
Control element
CMI1 = CMI2
Circuit 2
Residential
Distribution
CMI Reduction Project
Preventive Maintenance
Decision Process
Budget
Circuit Performance
Preventive Maintenance
Customer
Complaints
Improved Measurements 
Effective Results
Cost per Outcome
Desired Outcome
5
4
Lift
3
2
1
0
Opportunity
To Lower
O&M Expense
Cost of Program
Preventive Maintenance
Decision Process
Budget
Circuit Performance
Grid Design
Asset Health
Critical Load
Optimized Maintenance
Connectivity
Optimized Maintenance
Deteriorated Equipment
Impact on Grid Resiliency
1. THE GRID IS OLD—AND IT'S ONLY MAKING
MATTERS WORSE
According to the DOE report: "70% of the grid’s
transmission lines and power transformers are
now over 25 years old and the average age of
power plants is over 30 years."
As a result, "the age of the grid’s components
has contributed to an increased incidence of
weather-related power outages.“
. . . Utility Dive
Storm Influence on Transmission
http://nca2009.globalchange.gov/significant-weather-related-us-electric-grid-disturbances
Storm Impact on Reliability
Correlation: OH EQ CMI against MEDs
Customer Minutes of Interruption (CMI)
1,200,000
1,000,000
800,000
600,000
400,000
200,000
2002
2003
2004
2005
System MEDs
2006
2007
2008
Total OH EQ CMI
2009
2010
2011
Predictive Analytics 
Effective Conditions-based Maintenance
• Long Term Improvement in Reliability
– Measurable
– Documented
– Repeatable
• Additional Value
– GIS Data
– OMS Systems
– Software
• Complete Solution
–
–
–
–
Vegetation
Asset Data Collection
Condition Assessment
Predictive Maintenance
CONFIDENTIAL
Questions?
[email protected]
@EXACTERINC
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Applying Predictive Maintenance presented by John