power transformer asset management · engineering to project equipment remaining useful life •...

© 2016 Electric Power Research Institute, Inc. All rights reserved.

1. Michael Fourman – Georgia Transmission Company

2. Matt Walther & JinMing Liu – Con Edison, New York

3. Bhavin Desai - EPRI

Power Transformer

Asset Management


A Little About Georgia Transmission Corporation (GTC)

GTC is a “T” only cooperative.

Our fleet of transformers consists of just under 1000 units with an

average age of 26 years.

Average annual failure rate of 0.6% (about 6 a year)

With so few units and about 40 years of experience (GTC

formed in 1974) GTC does not have the breadth of expertise.

Use Transformer Oil Analyst (TOA) to store our oil results.

Acetylene triggered proactive action.

Other gasses might catch our attention.


Power Transformer Asset Management Issues

Consistent repeatable method for evaluating our fleet.

When to pull the trigger and replace a gassing or aged unit.

Finding the sweet spot is a function of risk tolerance.

Justification to replace a unit.

Objective justification tells a better story.

What to replace it with.

New versus used and the industries experience.

Budgeting.

Executives appreciate data supporting budgetary requests.


EPRI Tools to Address the Issues

Consistent repeatable method for evaluating our fleet.

Power Transformer Expert System (PTX)

When to pull the trigger and replace a gassing or aged unit.

PTX

Justification to replace a unit.

PTX, Industry-wide Database (IDB)

What to replace it with.

IDB

Budgeting

PTX, IDB

5© 2016 Electric Power Research Institute, Inc. All rights reserved.

Power Transformer Expert System (PTX)

What Is?

EPRI

PTX

Algorithms

Measurements• DGA

• Oil Quality

• Routine Electrical

Design

Information• Manufacturer

• Vintage

• Nameplate Data

Past

StressesMaintenance

History

Ab

no

rmal

Co

nd

itio

n In

dex

Paper Degradation Index

Readily Available

Data

Condition Indices -

Transformer Fleet

Risks

Belief & Likelihood of

Fault Conditions

Present

Basis for Operational and Asset

Management Decisions


Hazard Rates Failure ProjectionsUtility Data

0

0.002

0.004

0.006

0.008

0.01

0 20 40 60 80

HA

ZA

RD

RA

TE

AGE

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 1 2 3 4 5 6 7 8 9 10

PR

OB

AB

ILIT

Y

NUMBER OF FAILURES

0

2

4

6

8

10

12

14

1 6 11 16 21 26 31 36 41 46 51 56

CO

UN

T

AGE

Industry-wide Transformer Database (IDB)

What is?

IDB analysis can assess random and wear-out failures using calendar age as proxy for

transformer condition

Calendar age does not account for variations in operating history (primarily thermal stresses)

of specific transformers


GTC’s Vision for Asset Management Analytics

Continue using PTX and IDB as a supplemental tool for

decision making.

Use PTX.DLL to integrate with TOA, Doble, eDNA, and

Maximo.

Bring DGA, Power Factor, Load, Faults into PTX analysis.

Auto-creation of work orders if certain criteria met.

EPRI staff extremely helpful in using these tools!

Challenge

• How to relate observed wear out failure rates as a function

of calendar age to actual paper condition as reflected by

NDI?

Theoretical “Bathtub” Curve

Available Tools And Techniques

• IDB analysis can assess random and wear-out failures using calendar age as proxy for transformer condition. Calendar age does not account for variations in operating history (primarily thermal stresses) of specific transformers

• Normal (End of Life and Wear out) Failure: PTX assesses Normal

Degradation Index (NDI) to reflect insulation condition as function of evidence of degradation

• Abnormal (Infant and Quasi-Random) Failure: PTX also provides

indices to reflect potential incipient faults due to non-age related mechanism. Corresponds to “random” failures in the bathtub curve

0

0.005

0.01

0.015

0.02

0.025

0.03

0 20 40 60 80 100

Pro

bib

ilit

y o

f F

ailu

re

Age

Approach

• Retrospective studies of failed transformers and calibration

with constant “random” failure rate from IDB curves

provides assessment of individual transformer failure

probability due to non-age related failure mechanisms

Con Edison: Using PTX For Capital

Planning-Advanced Analytics

1. Correlation analysis on PTX NDI scores and transformer

failures* —using a snapshot of historical fleet NDI scores

To establish NDI as a proxy for overall transformer health/risk

2. Degradation analysis using NDI scores—using time

series of individual units

To identify transformers for future repair/replacements

To generate a forward looking transformer replacement plan

To project future transformer fleet risk profile

12*Transformer Failures as defined in EPRI’s Transformer Industrywide Database

A snapshot: correlation analysis on PTX

NDI scores and transformer failures

13

Failure rates increase exponentially as NDI scores increase.

*Our proactive replacement activities masked the possible correlation with NDI>0.3, as most of them were replaced

later on.

y = 0.0052e8.4482x

R² = 0.9845

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

0 0.05 0.1 0.15 0.2 0.25

An

nu

al

Fa

ilu

re R

ate

NDI

(257, 15)

(58, 6)

(15, 3)

(5, 2)

(population, failures)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50 60

ND

I

In Service Age (year)

36-T3

36-T5

A-T3

13-T14

MH-T3

Degradation analysis using NDI scores

• Degradation analysis is commonly used in reliability

engineering to project equipment remaining useful life

• Requirements: non-invasive, continuous degradation

indicators, and a pre-defined threshold of failure/end of

useful life

14

Predefined

Threshold Of

High Risk of

Failure

Retired in 2014

Winding damage Failed in 2012

Time Series degradation analysis

—Project Remaining Useful life

15

Transformer: 36-T3

Energized 4/25/1975

Projected date to reach NDI of 0.5: 2030

Estimated useful life remaining: 14

y = 0.0088e0.0730x

R² = 0.9410

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

10 15 20 25 30 35 40 45

ND

I

In-service age (year)

Extrapolating NDI growth trend may project the remaining

useful life of transformers with acceptable risk.

Transformer: MH-T3

Energized: 8/11/2004

Projected date to reach NDI of 0.5: 2019

Estimated useful life remaining: 3

y = 0.004974e0.311874x

R² = 0.992980

0

0.04

0.08

0.12

0.16

0.2

0 2 4 6 8 10 12

ND

I


Using PTX For Capital Planning

• The combination of the correlation between NDI and

transformer failures, and degradation analysis (trending of

NDI) may help us to forecast the risk profiles

– Identify future risky units

– Based on NDI scores, its trend and conditions of other components,

assign/forecast risk for today and future.

– Project proper annual replacement rate—avoiding replacement wall

16

y = 0.0088e0.2441x

R² = 0.9393

0.00

0.05

0.10

0.15

0.20

0.25

0 2 4 6 8 10 12 14

ND

I


y = 0.0052e8.4482x

R² = 0.9845

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

0 0.05 0.1 0.15 0.2 0.25

An

nu

al F

ailu

re R

ate

NDI

Annual Failure Rate (2007-2015) vs. 2006 NDI

(257, 15)

(58, 6)

(15, 3)

(5, 2)

(population, failures)


Ongoing and Future Research

0

0.005

0.01

0.015

0.02

0.025

0.03

0 20 40 60 80 100

Pro

bib

ilit

y o

f F

ailu

reAge

Normal Degradation Index

= f (Paper Insulation)Age Based Failure Rates Through fault Risk

Condition Based Failure Rates


Ongoing and Future Research

Integration: Enterprise Systems Algorithms Development

Validation Use of On Line Monitoring Data

Online

Offline


Together…Shaping the Future of Electricity

power transformer asset management · engineering to project equipment remaining useful life •...

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