improving distribution circuit performance without circuit ... · case study: unitil (t&d...

Improving Distribution CircuitPerformance without Circuit Rebuilds

John Lauletta, CEO

Maintenance StrategiesReliability Centered

Maintenance

ReactiveMaintenance (Run

To Failure)

PreventiveMaintenance (Time

Based)

PredictiveMaintenance

(Conditions Based)

ProactiveMaintenance

(Improvement)

• Small, non critical items• Inconsequential,

not likely to fail• Redundant

• Subject to wear• Consumable• Known failure pattern

• Random Failure Pattern• Not Subject to wear• PM Induced Failures

• Root Cause FailureAnalysis (RCFA)

• Failure Mode EffectsAnalysis (FMEA)

Predictive Maintenance (PdM)• To use data from an entire process to find any measurable

characteristics that may serve to warn that these detrimentalsituations are approaching.

• PdM techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should beperformed. This approach promises cost savings over routine ortime-based preventive maintenance, because tasks are performedonly 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 highersystem reliability.

Predictive Maintenance (PdM)

Visual Inspection

Infrared Detection

Ultrasonic DetectionRF Emission Grid Inspection

Impacts of Maintenance Strategies

Early FailurePeriod

Constant FailurePeriod

Wear-out FailurePeriod

Failu

re R

ate

Periodic/PreventiveMaintenance

PredictiveMaintenance

PdM – How?• To evaluate equipment condition, predictive

maintenance utilizes non-destructive testingtechnologies such as infrared photography,ultrasonic acoustic, radio frequency (RF)emissions, corona detection, vibrationanalysis, sound level measurements, oilanalysis, 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 a 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 ElectricDistribution System

Data Source:

32% of outagesare caused by trees

31% of outagesare caused by

failing equipment

Animals18%

Miscellaneous19%

Maintenance Techniques

32% of outagesare caused by trees

31% of outagesare caused by

failing equipment

Data Source:

Detecting Equipment Failure

Device• Insulator

FailureMechanism

• Internal Damage• Contamination• External Damage• Dry Band Arcing• Leakage• Tracking

FailureMechanism

Failure Detection TechnologyRF US-AC IR


Device• Lightning

Arrester

FailureMechanism

• MOV Damage• Contamination• External Damage• Dry Band Arcing• Leakage• Tracking

Failure Detection TechnologyRF US-AC IR

RF US-AC IR


Device• Cutout

FailureMechanism

• Internal Damage• Contamination• External Damage• Dry Band Arcing• Leakage• Tracking

Failure Detection Technology


Device• Transform

er FailureMechanism

• Bushing Damage• Contamination• Internal Arcing• Low Oil

Failure Detection Technology

RF US-AC IR

Detecting Equipment Failure• Use Technologies that measure specific failure

symptoms• Apply Technologies in most cost-effective

manner• Manage conditions-based analytics for

proactive maintenance action

Technical Tutorials

Case StudiesUtility Experience with PdM Programs

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

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

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

1. Patented Radio Frequency (RF) Technology– Captures PD & EMI emissions present in the field– Correlates emissions with GPS location

2. Proprietary Analysis (Failure Signature Library)– Analyzes and discriminates field data to identify specific

structures where arcing, leaking, & tracking are present

3. Ultrasonic Acoustic Technology– Field Engineers visit identified structure– Pinpoint component responsible for problematic conditions

Predictive Process

Exacter’s predictive process utilizes two technologies andproprietary analysis to identify non-temporary & consistent,problematic conditions that are related to the overheadelectric system

Process & Technologies

Exacter Radio Frequency Assessment

Failure Signature Analysis• Analyze & Discriminate Field Data• Identify emissions related to overhead

system• Specific location where PD/EMI is

present

• Data collection• Capture Partial Discharge & EMI• Correlate with GPS• Multiple passes

• Non-temporary• Consistent

Process & Technologies

The 1,161 RED FailureSignature Events are

captured by the EXACTERRF Assessment.

The Failure SignatureAnalysis identified 77 BLUE

Maintenance Groupswhere problematic

conditions (PD/EMI) arepresent.

Exacter Process & Technologies

Ultrasonic Field Locating

Exacter Process & Technologies

• Exacter Field Engineer visit identifiedstructure

• Confirm presence of PD/EMI• Identify specific component(s) responsible

for problematic condition

Deteriorated Equipment Population(7 year, 2 million structure survey)

U.S. Lightning Density

Regional DeterioratedEquipment Findings

KCP&L Located Equipment

Transmission Posts2%

Transformers2%

Dead Ends12%

Insulators4%

Lightning Arrestors7%

Cutouts3%

Pin Insulators69%

Misc HW1%

Lightning ArrestorsCutoutsDead EndsMisc HWPin InsulatorsInsulatorsTransformersTransmission Posts

Progress Energy Florida — Located Equipment

Non-Utility2%

TransPost Insulators

6%

Transformers6%

DistPost Insulators

15% Lightning Arrestors55%

Ground2%

Pin Insulators10%

Dead Ends4%

Lightning ArrestorsGroundTransformersDead EndsPin InsulatorsDist Post InsulatorsTrans Post InsulatorsNon-Utility

APS Located Equipment


Transformers4%

Dead Ends16%

Insulators4%


Cutouts10%

Pin Insulators42%

Misc HW1%


Western Located Equipment


Transformers3%

Dead Ends14%

Insulators6%

Lightning Arrestors0% Cutouts

6%

Pin Insulators57%

Misc HW0%


Baltimore


Cutouts2%

Deadends3%

Misc HW2%

Pin Insulators74%

Insulators0%

Transformers0%

Grounds2%

Lightning Arrestors

Cutouts

Deadends

Misc HW

Pin Insulators

Insulators

Transformers

Grounds

Baltimore

Lightning Arrestors4% Cutouts

12%

Deadends7%

Misc HW7%

Pin Insulators55%

Insulators4%

Transformers4%

Grounds7%

Lightning Arrestors

Cutouts

Deadends

Misc HW

Pin Insulators

Insulators

Transformers

Grounds


Transformers2%

Dead Ends12%

Insulators4%


Cutouts3%

Pin Insulators69%

Misc HW1%


Hidden Damage – No Protection

Obvious Physical DamageNo Protection Deterioration

Laboratory Specimen Test Setup

Arrester UnderTest

Antenna Arrayand Ground

Plane

RF EmissionInstrumentation

Adjustable HV ACSource

Comparing New andDeteriorated Arresters

• Current SensingResistor is 25 ohms

• Resistive voltagedivider = 1000/1

• Leading current istypical until cutoffvoltage is reached

• 58 Field samples werereviewed

New Specimen

Deteriorated Specimen

RF Emission Spectrum

• Linear Frequencyscale 0 to 1.5GHz

• Historical andInstantaneousanalysis shown

Deteriorated SpecimenEmission Spectrum

Demodulated RF Emission Analysis

Condition Signature Development

Onset of FailureSignature @ 0.67

MCOV

End of RF Signature@ 0.56 MCOV

Sensor correlatesdemodulatedemissioncharacteristics tofailure signature

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 # ofInterruptions % 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 formeaningful comparison of the circuits

– Divide Total EQ CMI / OH Miles for each circuit

– The new metric used to compare circuits is Equipment CMI / OHMile

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

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

Data Analysis: Exacter Pareto Analysis

Feeders that representgreatest opportunity forimprovement

2015 Pilot Assessment

The pilot covers portions of 67 circuits totaling 180 miles ofoverhead 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 equipmentrelated customer minutes of interruption (CMI)

- 29 Equipment related outages

Circuit Selection

2015 Pilot Assessment Area

2015 Pilot Assessment: Equipment Findings

Survey 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 equipmentreliability

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

• Visual damage is not typically apparent and does not necessarilyindicate 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 forutility maintenance planning

improving distribution circuit performance without circuit ... · case study: unitil (t&d...

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