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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
Trees / Vegetation32%
Animal Contact18%
Miscellaneous19%
Equipment Failure 31%
Source: U.S. DOE
Preventive Maintenance Decision Process
Grid Design Asset Health Connectivity
Budget
Circuit Performance Optimized Maintenance
PREDICTIVE (PdM)
Vegetation Mgmt.
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.
Visual Detection
Infrared Detection
Ultrasonic Emission Detection
RF Emission Detection
There are many benefits to conditions-based 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
Here are some ways to measure equipment condition.
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
Exacter, Inc. Provides: Grid Condition Assessment for Improved System Resiliency and Reliability
Initial Research 2004 to 2006Advanced Research Coninues
Research FacilitiesThe Ohio State University High Voltage Laboratory
Test Fixtures
Two views of the test setup
Surge Arrester Being Studied
Lab Workstation
EXACTER® Sensor
Research Analytics
Faraday Cage
Exacter Acquisition and Analysis ProcessData Acquisition &
DiscriminationData Analysis
Actionable Information
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
Data analyzed for severity, persistence and prevalence, enabling: • Exact locating of failing
component• Replacement prioritization
Precise GPS coordinates and relevant condition-
data transmittedto 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 ControlCondition 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
• 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
Internal Algorithms: Geographic Circle Calculation
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
Vegetation, 32%
Animals and Other, 37%
Selected Prior-ity Feeders to
Assess and Improve Af-
fecting 20% of Out-
ages
Deferred, Less Critical , Low SAIDI Impact Feeders 11% Outage Causes
PredictiveBased
Maintenance
30%
26%
17%
9%8%
5%
2% 2%1% 0%0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
-
5,000,000.00
10,000,000.00
15,000,000.00
20,000,000.00
25,000,000.00
1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251
Eastern Division
CMI Impact AnalysisCircuit CMI Contribution
5,950 OVHD Miles
Example: Prioritized Worst Performing Circuit (WPC) Improvement Program
73% of Total CMI – 1,904 miles (32%)
CMI Result ofCurrent Programs
Sum of All Customer Interruption DurationsTotal Number of Customers ServedSAIDI
71.1861.33 63.92
23.38
Aggregate Customer Experience
IEEE 1366
Total # of Customer InterruptionsTotal Number of Customers ServedSAIFI
1.52 1.48 1.53
1.03
Aggregate Customer Experience
Sum of All Customer Interruptions Total Number of Customer Interruptions CAIDI
Aggregate Customer Experience
46.73 41.32 41.90
22.78
Relatively No Change in the Customer Experience
Non Storm-Related Outages on the Electric Distribution System
Trees / Vegetation32%
Animal Contact18%
Miscellaneous19%
Equipment Failure 31%
Source: U.S. DOE
Flat Response = Challenges & Opportunities
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I
Flat Response = Challenges & Opportunities
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I
Target Performance
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I
Top Decile20 Years of Design Excellence
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I
SAIDI FocusO&M – Workforce Deployment
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I
SAIFI FocusCapital Intensive Programs
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I
Typical Strategies
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I Tree Trimming
Automat ion
60% – 70%Out of ROW
Replace Deteriorated Equipment
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
I Tree Trimming
Automat ion
Predictive Equipment Maintenance
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50
50
100
150
200
250
300
350
400
450
500
SAIFI
CAID
IWhat is The Strategy to Improve?
How Good Is Good Enough?
• SAIDI (CMI)
• SAIFI (Number of outages)
• CAIDI (CMI)
• CEMI (Number of outages)
• Targeted Performance: 1st Quartile or Decile
• Stay Ahead of the Bear
What is Urgent and Important?
Informed Maintenance Decisions
Circuit A Circuit B Circuit C Circuit D0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
CMICMI (
100,
000)
Optimized Selection
Circuit A Circuit B Circuit C Circuit D0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
CMICMI / Mile
CMI (
100,
000)
Circuit Connectivity
1,000 Customers
CMI1 = CMI2
120 Customers
Circuit 1
Circuit 2
Circuit Physical Design
9 miles of OH 1 mile of UG
9 miles of UG1 mile of OH
CMI1 = CMI2
Circuit 1
Circuit 2
Circuit Critical Connectivity
100 Customers
100 Customers
CMI1 = CMI2
Circuit 1
Circuit 2
Grid Operation Importance
Smart Grid Control element
Residential Distribution
CMI1 = CMI2
Circuit 1
Circuit 2
CMI Reduction Project
Preventive Maintenance Decision Process
Budget
Circuit Performance
Preventive Maintenance
Customer Complaints
Improved Measurements Effective Results
00.5
11.5
22.5
33.5
44.5
Cost per Outcome
Cost of Program
Des
ired
Out
com
e
0
1
2
3
4
5
Cost per Outcome
Cost of Program
Des
ired
Out
com
e
Lift
OpportunityTo Lower
O&M Expense
Preventive Maintenance Decision Process
Grid Design Asset Health Critical Load Connectivity
Budget
Circuit Performance Optimized Maintenance
PREDICTIVE (PdM)
Optimized Maintenance
Deteriorated Equipment Impact on Grid Resiliency
1. THE GRID IS OLD—AND IT'S ONLY MAKING MATTERS WORSEAccording 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
http://nca2009.globalchange.gov/significant-weather-related-us-electric-grid-disturbances
Storm Influence on Transmission
Storm Impact on Reliability
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 -
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Correlation: OH EQ CMI against MEDs
System MEDs Total OH EQ CMI
Cu
sto
mer
Min
ute
s o
f In
terr
up
tio
n (
CM
I)
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
