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Asset Management of MV Cables using Data Driven Health Indices
for Water TreeingNigel Hampton, Josh Perkel, Dean Williams
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10th International Conference on Insulated Power Cables
• (MV) XLPE insulated cables were first installed in the early 1960’s.
• Expected to perform reliably for ≈30 yrs.
• Not aware that moisture, voltage stress, imperfections would combine to grow water trees.
• Many cables failed after few years.
• This impacted operating costs that electric utilities are still dealing with today.2
Background
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10th International Conference on Insulated Power Cables
Prior Work to Historical Archive• >450 different examinations
• >40 Utilities
• Individual Reports -> Database
• >1500 large Vented Trees
• >3200 large Bowtie Trees
• Meta Data
• Generation
• Material
• Neutral Condition
• Prior Failures
• 21,000 data entries
Summary of Largest Water Trees Sites Identified
(Wafer or Hot Oil)
Tree Type
Tree
Length
(mils)
% Growth
through WallInitiation Point
Example 1
BT 100 38 5 mil Void
BT 67 26 9 mil Contaminant
ISAT 88 34 Unknown
BT 35 13 11 mil Contaminant
CSAT 80 31 Unknown
Example 2
BT 27 10 Small Particle
Example 3
ISAT 254 Full Unknown
BT 30 12 Small Particle
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10th International Conference on Insulated Power Cables
Basic AssessmentsCables are extracted from the field
• Failed
• Siblings of Failures
• Concerning
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10th International Conference on Insulated Power Cables
Evolution of Tree Length – Measured Data
• Age data are generally
available
• These are grouped to
determine the distribution of
lengths of trees in “Age
Bins” – Mean Lengths
• These estimates use all
available tree lengths
Boxes represent the
size of the age bins
and the 95%
Confidence Limits
on the Mean
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10th International Conference on Insulated Power Cables
Diagnostic Process to Manage the AssetGenerate
Data
Collate
Data
Calculate
Features
• Longest
• Median
• Density
• Generation
Health Index Algorithm
Calculated
Features
Rules of Thumb(Heuristics)
Classification Algorithms(Machine Learning, Pattern Recognition)
• Water Tree
• Develop Meta Data
SAIFI of selected countries vs % underground
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10th International Conference on Insulated Power Cables
Measured Tree Diagnostic - Heuristic
Heuristic
Engineering Expertise
Trees > 50% of insulation
correlate with Failure in Service
Trees
<50%
Trees
>50%
Survived in
Service *Failed in
Service *
Overall 49%
Trees
<50%
Trees
>50%
Survived in
Service 80 32Failed in
Service 76 24
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10th International Conference on Insulated Power Cables
Use More Information ?Longest Vented
Median Vented
Number of Vented
Longest Bowtie
Median Bowtie
Number of Bowtie
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10th International Conference on Insulated Power Cables
Health Indices for Asset Management
• Health Indices summarise many inputs (Treeing, Age, History, Generation, etc)
• Most are simple “Rules of Thumb” developed based on expert opinion
• In this work large datasets are available hence the most appropriate way forward is to use a “Machine Learning / Algorithmic” approach
• Determine the Weights (a, b, c,.. , .. ,..)
• For
• Length – Bowtie & Vented, Longest & Median
• Density - Bowtie & Vented
• Meta Data – Generation, Age
𝑯𝑰 = 𝜶 𝑳𝒆𝒏𝒈𝒕𝒉 + 𝜷 𝑫𝒆𝒏𝒔𝒊𝒕𝒚 + 𝜸 𝑮𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏 +. . +. . +. . +. .
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10th International Conference on Insulated Power Cables
Evolving Cable Design Solutions
Generation Insulation Semicons Jacket Barrier
0 Paper Tape Carbon Tape Jacket Extruded Lead
1 Thermoplastic
HMPWE
Graphite / Carbon Tape
None
None
2 Extruded Thermoplastic
3XLPE
or
EPR
Graphite / Carbon Tape
4 Extruded Thermoplastic
5
Extruded Thermoset
(crosslinked)
6
Jacket7 WTR
XLPE
or
EPR
8 Conductor Blocking
9 Blocking / Metal Barrier
10 ? ? ? ?
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10th International Conference on Insulated Power Cables
Weightings for Water Tree FactorsLVT
MVT
DVT
LBT
MBT
DBT
GEN
AGE
1 3 BOWTIE
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10th International Conference on Insulated Power Cables
Health Index – Algorithm & Machine Learning
Health Index
Feature Creation
Complexity Reduction
Predict
Survive Fail
Survived in
Service 74 24Failed in
Service 35 65
Overall 70%
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10th International Conference on Insulated Power Cables
Generating Context• Individual Health Index (HI)
• HI’s have meaning – large values = more / big trees = poorer performance
• Provide a context for future measurements
• Auto update with new data
• Provides a “relative prioritization”
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10th International Conference on Insulated Power Cables
Case Study
• 8 cable investigations for 1979 vintage cables
• None of these cables have experienced a failure in service
• Interested in relative health and what actions are suggested by experience
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10th International Conference on Insulated Power Cables
Case Study
• 8 cable investigations for 1979 vintage cables
• 4 - 5 in “No Immediate Action Required”
• 2 - 3 in “Watch”
• 1 in “Action Required”
A 50% reduction
opportunity in
the O&M budget
of today
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10th International Conference on Insulated Power Cables
Conclusions
• Collated data is the basis for analyses – datamining is worth the effort• Brings insights
• Enables testing of “Heuristics / Tribal Knowledge”
• Data and Expertise derived Health Indices• Outperform simple Heuristics
• Capture valuable knowledge
• Health Indices provide• Context
• Transparent decision making
• There is a way to make use of the information that come from forensic cable analyses