Predictive Maintenance for Improved Grid Performance · Predictive Maintenance for Improved Grid...

Predictive Maintenance for Improved Grid Performance

Presenter: John Lauletta – Exacter, Inc.

Technical Tutorials 1

Agenda

• Predictive Maintenance Basics

• Creating PdM Business Cases

• Assessing the Grid

• Field Findings

• Case Studies


Predictive Maintenance BasicsWhat is it and Why Use It?


Predictive Maintenance (PdM)

• To use data from an entire process to find any measurable characteristics that may serve to warn that these detrimental situations are approaching.

• PdM techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.

• PdM inspections are performed while equipment is in service, thereby minimizing disruption of normal system operations. Adoption of PdM can result in substantial cost savings and higher system reliability.


Predictive Maintenance (PdM)

Visual Inspection

Infrared Detection

Ultrasonic Detection

RF Emission Grid Inspection


The Structure of Maintenance Strategies


Impacts of Maintenance Strategies


Early FailurePeriod

Constant FailurePeriod

Wear-out FailurePeriod

Failu

re R

ate

Periodic/Preventive Maintenance

Predictive Maintenance

PdM – How?

• To evaluate equipment condition, predictive maintenance utilizes non-destructive testing technologies such as infrared photography, ultrasonic acoustic, radio frequency (RF) emissions, corona detection, vibration analysis, sound level measurements, oil analysis, and other specific online tests.


PdM Why? Benefits of PdM Strategies

• Maintenance costs - down by 50%

• Unexpected failures - reduced by 55%

• Repair and overhaul time - down by 60%

• Spare parts inventory - reduced by 30%

• 30% increase in machinery MTBF

• 30% increase in uptime


Applying PdM Strategy to the Grid

1. Assess System Condition2. Schedule Maintenance3. Measure Results4. Repeat Process


Animals18%

Miscellaneous19%

U.S. Non Weather-Related Outages on the Electric Distribution System

Data Source:

32% of outages are caused by trees

31% of outagesare caused by

failing equipment


http://www.doe.gov/index.htm

Animals18%

Miscellaneous19%

Maintenance Techniques

32% of outages are caused by trees

31% of outagesare caused by

failing equipment


Data Source:


Detecting Equipment Failure

Device

• Insulator

Failure Mechanism

• Internal Damage

• Contamination

• External Damage

• Dry Band Arcing

• Leakage

• Tracking


• RF/USAC Emission






Failure Detection


Device

• Lightning Arrester

Failure Mechanism

• MOV Damage

• Contamination

• External Damage

• Dry Band Arcing

• Leakage

• Tracking


• RF/USAC/IR Emission






Failure Detection


Device

• Cutout

Failure Mechanism

• Internal Damage

• Contamination

• External Damage

• Dry Band Arcing

• Leakage

• Tracking








Failure Detection


Device

• Transformer

Failure Mechanism

• Bushing Damage

• Contamination

• Internal Arcing

• Low Oil






Failure Detection


• Use Technologies that measure specific failure symptoms

• Apply Technologies in most cost-effective manner

• Manage conditions-based analytics for proactive maintenance action


Creating A PdM Business CaseChoosing an Effective Strategy


Common Utility Focus Areas• Worst Performing Circuits

• Customer Satisfaction Improvement

• CMI or other related indices

• Circuit Rebuild Priority

• Smart Grid and/or AMI Preparation

• Geography

• Critical Loads

• Equipment Type – i.e. surge arrester only, porcelain cutouts, etc.


Elements of Return on Investment (ROI)

• SAIDI Reduction – Value of a SAIDI Minute

• CMI Reduction – Value of a Customer Minute of Interruption (CMI)

• Economic Cost of Outage to Customers


Strategic Need

Utility

Director

Grid CMI Data

Exacter

Analyst

Opportunity

Utility

Executive

ReduceSAIDI

Proposal


Executive Summary

ProgramObjective

# 3P Miles

# Circuits

Projected Impact

Units/Cost

A. System-Wide SAIDI Reduction StrategyOptimize overall reliability improvementCriteria: Top 613 Circuits: CMin/Mile

7,000 6131.8 to 3.6 SAIDI Min

System-wide

B. Divisional SAIDI Improvement StrategyDivisional targets for reliability improvementCriteria: Bottom 3 Divisions : Divisional SAIDI

9,000 3304.6 to 9.2 SAIDI Min

Targeted Population

C. Focused Customer Experience StrategyTargeted EQ-Related WPCsCriteria: Top 119 Circuits: Circuit SAIDI

5,000 11830 to 60

SAIDI MinTargeted Population


A. Holistic/Strategic: System-Wide CMin/SAIDI ReductionBased on Top-Ranked Equipment-Related CMin/MILE CircuitsPresents optimal strategy at 57% of the miles of 1.0

PROJECT SUMMARY PROJECT SCOPE % OF TOTAL

EQ CMin Total 100,058,262 73%

EQ CESO Total 998,040 75%

OH 3P Miles 8,113 7%

Circuit Count 613 20%

# Customers 1,548,757 28%

TARGETED SAIDI 18.12 max system-wide potential

PROJECTED SAIDI SAVINGS 1.8 to 3.6 projected 10%-20% result*

*actual field results showing 20%-40% YoY improvements

PROJECTED SAIDI REDUCTION 2.72 @ 15% improvement

PROJECTED CMin REDUCTION 15,008,739 @ 15% improvement

PROJECT BENEFITS

SYSTEM-WIDE SAIDI IMPROVEMENT - TOP EQ CMin/MILE


A. Holistic/Strategic: System-Wide CMin/SAIDI ReductionBased on Top-Ranked Equipment-Related CMin/MILE Circuits

Data

Service Division (if applicable) or Subtation IDCircuit / Feeder NameSum of Equipment CMI Per Mile Sum of OH 3-phase Miles Sum of Feeder Customers Served Count of Circuit / Feeder Name

SAN FRANCISCO 1,134,580 105 137,992 56

PENINSULA 802,670 231 115,426 52

EAST BAY 653,789 243 137,026 58

MISSION 475,089 141 103,005 31

SAN JOSE 385,667 198 138,883 38

DE ANZA 378,159 166 75,350 27

SIERRA 337,912 501 40,366 20

NORTH BAY 298,737 436 90,732 34

DIABLO 258,699 151 88,648 32

KERN 243,368 415 72,640 32

CENTRAL COAST 214,034 794 120,033 35

FRESNO 208,942 681 86,921 42

SACRAMENTO 187,202 668 70,029 34

STOCKTON 172,458 406 47,100 30

YOSEMITE 142,533 912 55,060 24

HUMBOLDT 141,634 488 32,361 16

LOS PADRES 113,246 919 71,959 22

NORTH VALLEY 104,409 439 24,063 18

SONOMA 76,358 218 38,819 14

Grand Total 6,329,487 8,111 1,546,413 615


4.0 Tactical: Improved Customer ExperienceFocused on circuit SAIDI WPCsCircuits Experiencing > 200 EQ-Related SAIDI

PROJECT SUMMARY PROJECT SCOPE % OF TOTAL

EQ CMin Total 25,067,758 18%

EQ CESO Total 160,127 12%

OH 3P Miles 5,487 5%

Circuit Count 119 4%

# Customers 83,237 2%

TARGETED SAIDI 301.16 FOR TARGETED POPULATION

PROJECTED SAIDI SAVINGS 30 to 60 projected 10%-20% result*

*actual field results showing 20%-40% YoY improvements

PROJECTED SAIDI REDUCTION 45.17 @ 15% for targeted population

PROJECTED CMin REDUCTION 3,760,164 @ 15% for targeted population

CIRCUITS EXPERIENCING MORE THAN 200 EQ SAIDI

PROJECT BENEFITS


4.0 Tactical: Improved Customer ExperienceFocused on circuit SAIDI WPCsCircuits Experiencing > 200 EQ-Related SAIDI

Row Labels Sum of Equipment Circuit SAIDI Sum of Total OH Miles Sum of OH 3-phase Miles Sum of Feeder Customers Served Count of Circuit / Feeder Name

NORTH BAY 19,307 6 0 10 1

SAN FRANCISCO 12,030 22 5 6,432 4

KERN 8,736 1,317 906 6,802 24

FRESNO 6,605 1,564 1,098 8,622 20

CENTRAL COAST 5,343 331 278 2,225 9

NORTH VALLEY 3,660 834 436 5,399 9

SIERRA 2,781 351 235 3,861 6

YOSEMITE 2,704 862 625 7,660 10

SACRAMENTO 2,325 947 728 5,697 8

HUMBOLDT 2,253 620 305 12,647 7

LOS PADRES 2,070 930 467 9,475 6

STOCKTON 1,584 242 177 1,393 5

SONOMA 712 198 71 6,797 2

EAST BAY 672 19 5 1,154 3

MISSION 519 12 3 3,424 2

DE ANZA 344 3 1 979 1

DIABLO 238 - 3 11 1

Grand Total 71,884 8,259 5,343 82,588 118


Sustained Outages (SO):• EQ-related rank highest• Unknown issues are 83% of EQ

issues – likely presents another area of focus as we proceed


Customer Minutes of Interruption (CMin)• EQ-related ranks highest (again)• Unknown issues are 36% of EQ issues


• Based in Columbus, OH

• US / Canada Alliance Partners

• International Alliance Partners

Australia, Mexico

• 5 U.S. Patents, 7 Int’l Patents

• 2 million+ Poles Surveyed

• 3rd Party Validation

• DOE

• NETL

• The Ohio State University

Grid Condition Assessment Priorities for Improved System Resiliency and Reliability



Exacter Process & Technologies

• Visit identified structure

• Confirm presence of PD/EMI

• Identify specific component(s) responsible for problematic condition

Condition Assessment

Strategic Need

Utility

Director

Grid CMI Data

Exacter

Analyst

Opportunity

Exacter

Survey

Grid Conditions

Contract

Exacter

Field Eng.

Condition Causes

Utility

Operations

New Grid CMI Data

Utility

Executive

ReduceSAIDI

Utility

Operations

Grid Repairs

Exacter

Analyst

PriorityCriteria

GIS CircuitPriority

ProposalField

Report

OptimalMaint.

Plan


Research FacilitiesThe Ohio State University High Voltage Laboratory


The 31% Problem

All Studies Agree:

31% of Non-Storm Related Outages in America are due to Deteriorating Equipment


http://www.eei.org/

http://www.ieee.org/portal/site/iportals


http://www.dteenergy.com/

http://www.appanet.org/index.cfm

http://www.nreca.org/

Grid Conditions-Based AssessmentWhat is it?

• A strategic and analytic assessment of Overhead circuits that accurately identifies opportunities to improve circuit performance, reliability, and grid resilience

How Does it Work?

• A collaborative process that incorporates utility outage information, technology, and analytics to accurately assess overhead systems

Benefits of PdM on the Grid

• Grid Condition Assessmentsprovides strategic, specific, and actionable information to improvecircuit performance

• Reduce CMI and related indices

• Optimize outage information

• Prioritize maintenance operations

• Reduce costs association with outagerestoration

Example Long Term Trend Analysis

Index2011 2012 2013 2014

SAIDI (minutes)71.18 61.33 63.92 23.38

SAIFI (# of outages)1.52 1.48 1.53 1.03

CAIDI (minutes)46.73 41.32 41.9 22.78

MAIFI (interruptions)5.48 6.13 5.83 4.21


Utility Sample Outage Statistics


Optimized Project Design


Deteriorated Equipment Population

(7 year, 2 million structure survey)


U.S. Lightning Density



Regional Deteriorated Equipment Findings

KCP&L Located Equipment

Transmission Posts

2%

Transformers

2%

Dead Ends

12%

Insulators

4%

Lightning Arrestors

7%

Cutouts

3%

Pin Insulators

69%

Misc HW

1%

Lightning Arrestors

Cutouts

Dead Ends

Misc HW

Pin Insulators

Insulators

Transformers

Transmission Posts

Progress Energy Florida — Located Equipment

Non-Utility

2%Trans

Post Insulators

6%

Transformers

6%

Dist

Post Insulators

15%Lightning Arrestors

55%

Ground

2%

Pin Insulators

10%

Dead Ends

4%

Lightning Arrestors

Ground

Transformers

Dead Ends

Pin Insulators

Dist Post Insulators

Trans Post Insulators

Non-Utility

APS Located Equipment

Transmission Posts

10%

Transformers

4%

Dead Ends

16%

Insulators

4%

Lightning Arrestors

13%

Cutouts

10%

Pin Insulators

42%

Misc HW

1%

Lightning Arrestors

Cutouts

Dead Ends

Misc HW

Pin Insulators

Insulators

Transformers

Transmission Posts

Western Located Equipment

Transmission Posts

14%

Transformers

3%

Dead Ends

14%

Insulators

6%

Lightning Arrestors

0% Cutouts

6%

Pin Insulators

57%

Misc HW

0%

Lightning Arrestors

Cutouts

Dead Ends

Misc HW

Pin Insulators

Insulators

Transformers

Transmission Posts

Baltimore

Lightning Arrestors

17%

Cutouts

2%

Deadends

3%

Misc HW

2%

Pin Insulators

74%

Insulators

0%

Transformers

0%

Grounds

2%

Lightning Arrestors

Cutouts

Deadends

Misc HW

Pin Insulators

Insulators

Transformers

Grounds

Baltimore

Lightning Arrestors

4% Cutouts

12%

Deadends

7%

Misc HW

7%

Pin Insulators

55%

Insulators

4%

Transformers

4%

Grounds

7%

Lightning Arrestors

Cutouts

Deadends

Misc HW

Pin Insulators

Insulators

Transformers

Grounds

Transmission Posts

2%

Transformers

2%

Dead Ends

12%

Insulators

4%

Lightning Arrestors

7%

Cutouts

3%

Pin Insulators

69%

Misc HW

1%

Lightning Arrestors

Cutouts

Dead Ends

Misc HW

Pin Insulators

Insulators

Transformers

Transmission Posts

Contamination Flashover

• Largest component of unknown problems

• Difficult to overcome without a PdM strategy

• Creates Fuse Blows, Breaker Operations, and Recloser Operations


Case Study: PG&E (EEI TD&M, Oct, 2016)


Case Study: UNITIL (T&D World, Oct. 2015)


Case Study: COSERV (RE Magazine, Oct. 2016)


Hidden Damage – No Protection


Obvious Physical Damage

No Protection Deterioration


Laboratory Specimen Test Setup

Arrester Under Test

Antenna Array and Ground Plane

RF Emission Instrumentation

Adjustable HV AC Source


Comparing New and

Deteriorated Arresters

• Current Sensing Resistor is 25 ohms

• Resistive voltage divider = 1000/1

• Leading current is typical until cutoff voltage is reached

• 58 Field samples were reviewed

New Specimen

Deteriorated Specimen


RF Emission Spectrum

• Linear Frequency scale 0 to 1.5 GHz

• Historical and Instantaneous analysis shown

Deteriorated SpecimenEmission Spectrum


Demodulated RF Emission Analysis

Condition Signature Development

Onset of Failure Signature @ 0.67

MCOV

End of RF Signature @ 0.56 MCOV

Sensor correlates demodulated emission characteristics to failure signature


Sensor Evaluation

• Results of 170 samples in 10 second intervals for one arrester

• Analysis frequency range from 60 to 3 kHz

• Vertical scale uncalibrated sensor signature correlation statistic Emission Data Evaluation

One Specimen


Central Texas Service Territory

• 6 counties

190,000+ customers served

4,600 Distribution Miles

• 2,200 Overhead Miles

• 2,400 Underground Miles

Case Study – South Central U.S.


Data Analysis: Historical Interruption Data

The customer provided the most recent 12 months of interruption data

• October 1, 2013 – September 30, 2014

• IEEE Equipment Cause Codes:

• 300 – Material or Equipment Failure

• 400 – Decay/Age of Material or Equipment

• 410 – Corrosion/Abrasion of Material or Equipment

Outage Cause CMI# of

Interruptions% of CMI

Equipment 1,619,925 85 33% (Approx)

** Excludes IEEE days for MEDs // Excludes Momentaries


Data Analysis: Exacter Pareto Analysis

• Exacter Analysis normalizes the dataset to allow for meaningful comparison of the circuits

• Divide Total EQ CMI / OH Miles for each circuit

• The new metric used to compare circuits is Equipment CMI / OH Mile

• Exacter ranks each circuit by the Equipment Related CMI / OH Mile

• Circuits with the highest Equipment CMI / OH Mile provide the greatest opportunity to improve performance and reliability


Data Analysis: Exacter Pareto Analysis

Feeders that represent greatest opportunity for improvement


2015 Pilot Assessment

The pilot covers portions of 67 circuits totaling 180 miles of overhead distribution

- The majority of overhead miles are 3-phase infrastructure- Eastern area of Service territory- High growth area

The option has the opportunity to impact 993,338 equipment related customer minutes of interruption (CMI)

- 29 Equipment related outages

# of

circuits

# of OH

Miles

% of OH

Miles

Equipment

CMI

% of Equipment

CMI

Estimated #

of Repairs

2015 Assessment 67 180 8.5% 993,338 61.3% 20

Circuit Selection: Pareto & Geographic

** Excludes IEEE days for MEDs // Excludes Momentaries

Circuit Selection


2015 Pilot Assessment Area


2015 Pilot Assessment: Equipment Findings

Component # of findings % of findings

Bushing on Capacitor 1 1.5%

Bushing on Transformer 1 1.5%

Lightning Arrester 27 40.3%

Pin Insulator 32 47.8%

Oil Switch 1 1.5%

Switch 1 1.5%

Transformer 2 3.0%

Post Insulator 1 1.5%

Strain Insulator 1 1.5%

Total Component Finds 67

2015 Pilot AssessmentSurvey Results


2015 Pilot Assessment: Component Finds

• Exacter assessment identified 1 component every 2.68 miles

– 3-phase overhead // co-located circuits // overbuild

– Density of service territory

– Presence of protective devices on system

• Exacter assessed 6,446 poles

– 180 assessed miles = 6,446 poles

– 67/6,446 = 1.03% of poles with problematic conditions

– 98.97% of assessed infrastructure does not have presence of problematic conditions


2015 Pilot Assessment: Field Report


2015 Pilot Assessment:

Considerations & Conclusions

• May 2015: “Wettest” month on record for Dallas-Ft. Worth area• 4.4 million lightning strikes

• More than 2013 or 2014 total

• Correlate lightning strike data with Exacter assessment

• Maintenance Operations for Identified Components • Further Lightning Arrester investigation

• Identify use for Exacter assessments in future maintenance operations


Summary and Remaining Work

• Arresters are a critical component in electric grid equipment reliability

• Arresters are most deteriorated in areas of greatest need: Southeast/Northeast U.S.

• Visual damage is not typically apparent and does not necessarily indicate state of protection element

• RF emissions characterize deterioration that impacts performance

• In 58 samples, 57 showed deterioration of protective ability

• More field samples will be evaluated in laboratory conditions to optimize failure signature discrimination and source location for utility maintenance planning


Predictive Maintenance for Improved Grid Performance · Predictive Maintenance for Improved Grid...

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