2 thanapong asset mgmt tr

The Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut’s University of Technology, North Bangkok (KMUTNB)

Asset Management of Power Transformer – Optimization of Operation and Maintenance Costs

Asst.Prof.Dr.-Ing. Thanapong Suwanasri

25 October 2013

Presenter

Presentation Notes

2 Thanapong Suwanasri

Asset Management of Power Transformer

Introduction

Working Procedure

Failure Statistics, Database Management

Condition Assessment

Risk Assessment

Spare Part Management

Conclusion

Contents



Introduction Demands on high reliability, good power quality and acquiring more benefits

from electrical asset asset management approach cost reduction

Risk assessment of power transformer should be focused due to high

acquisition, maintenance cost and its catastrophic failure consequences.

Power transformer has been maintained by preventive maintenance.

Condition-based maintenance is performed according to its known condition.

Risk-based maintenance considers the condition and importance of equipment.



Introduction

Objective

Risk based maintenance Condition + Importance

To optimize spare parts and minimize inventory cost.



Power Transformer Asset Management Process Asset Management

StrategiesAsset Information

Financial Information

Condition Assessment

Risk Management

Economic Risk Management

DecisionsOptimized maintenance strategies

Network Data

Importance Assessment



Power Transformer Asset Management Process Database Setup systematic record of periodic test and visual inspection

Condition-based Maintenance

Importance Index

Risk-based Maintenance



Failure Statistics

To determine critical components and failure causes

The well-known Weibull distribution method is applied.

1

( ) ,ttf t e t

ββ γηβ γ γ

η η

− −− −

= ≥

( ) 1 ,tt

F t e

βγ

η γ −

− = − ≥

t, )(1)( γ

β

ηγ

≥=−=

−−

t

etFtR

1

)()()(

−

−

==

β

ηγ

ηβλ t

tRtft

]11[.β

ηγ +Γ+=MTBF

(1)

Probability Distribution Function (PDF)

Cumulative Distribution Function (CDF)

Reliability; R(t)

Failure Rate; λ(t)

Mean Time between Failures (MTBF)



Defective components of 230/115 kV, 200 MVA Defective components of 115/22 kV, 50 MVA

Transformer 230/115 kV, 200 MVA has 117 units with 30 failure records

Transformer 115/22 kV, 50 MVA has 186 units with 59 failure records



Database Management For a convenient and systematic data record in the central database server.

Integrated web application for using as decision support tool.



Condition Assessment Components are classified into 7 groups.

Active Part

Insulating Oil

Bushing

Arrester

Diagnostic tests are used to access the component condition. Electrical Test

Insulating Oil Test

Visual Inspection

Scoring and weighting technique is applied Component HI

( )

( )100

WS

WS%Index n

1iimax

n

1iii

i

∗∗

∗=

∑

∑

=

=

OLTC

Tank

Protective Devices

Si =Score of each test Smax,i=Max. score of each test Wi =Weightng factor n =Number of diagnostic tests



Condition Assessment The component condition and weighting are used to evaluate the overall

condition or overall HI.

100)(%1

∗∗= ∑=

=

mj

jjj HIWHI

Main Tank

ProtectionActive Part Arrester Insulating Oil

OLTCBushing

Overall Condtion



Risk Assessment Evaluation Criteria of Transformer Importance

Load criticality

Impact on system stability

Possibility of failure

Failure consequence

Damage of property

Social impact

Environmental concern

The criteria are scored into low, moderate and high importance.

The criteria with more impacts to supply interruption and system availability will

be assigned with higher weighting factor.



Risk-based Maintenance Combining the condition and importance index to create risk matrix.

Maintenance strategies of each zone can be setup appropriately.

The risk is measured by distance ‘d’ with respect to 45O reference line.

Maintenance Strategies:

1=Repair/Replace when fail (without blackout)

2=Replace/Repair/Refurbish by economic condition

3=Replace/Repair/Refurbish immediately

4=Corrective Maintenance (CM) with normal maintenance

5=Time-based maintenance (TBM) and normal maintenance

6=Condition-based maintenance (CBM) and online monitoring

7=CM with routine inspection

8=TBM

9=TBM and CBM



method to optimize number of spare parts for effective inventory control.

Using Pareto analysis, components of power transformer in the stock are

classified as ABC classes.

• Class A means a few most expensive ones that need special care.

• Class B means ordinary ones that need standard care.

• Class C means large number of cheap items that need little care.

Spare Part Management



Spare Part Classification Class A: • Items are account for 80% of the total inventory cost but about 20% of total inventory items. • High capital investment and requires a close control. • Due to high cost of these items, a minimum safety stock is maintained. • Class A are busing, arrester and on-load tap changer.

Class B: • Items are account for 20% to 30% of the total cost but about 20% to 30% of total items. • The class B item of power transformer is insulating oil. • Economic order quantity should be applied.

Class C: • Items are account for 20% of the total cost but about

80% of total items. • This item has the cheapest price and needs a minimal

control. • E.g. seal, gasket, bolt and nut.



Work Procedure and Calculation

High voltage equipment

Determination of major or critical components and auxiliary components

Component classification by Pareto diagram

Class A Class CClass B

Statistical distribution techniques

Economic order quantity Two – bin policy



A. Statistical Distribution Techniques for Class A Management

Normal distribution technique Highly accurate technique, when number of recorded failures is sufficient.

Information needed for the analysis First energized date must be known.

Calculation Firstly, the number of service year (sum of operating time that every piece of component is in service).

Then, the failure rate is calculated. Systematic failure record

Finally, calculate the optimum stock for smooth operation within the equipment lifetime (N)

M = number of items T = lifetime of considered item. Z can be obtained from the pre-

calculated standardized normal tables, e.g. if availability in stock is required as 99%, then Z is equal to 2.33.

( )N M T T Zλ λ= ⋅ + ⋅ ⋅



B. Poisson distribution technique The percentage of stock reliability (RI ) and availability of items (I) in the stock within the time interval T.

0%

I

I ii

R P=

=∑

' 1λµ

=

• MTTR is mean time to repair. • M is number of the items used in the system. • Ti is interval of observation • Nf is number of failure. • A is expected number of demand. • T is lead time of stock ordering. • λ’ is the replacement rate. • I is number of the items kept in the stock.

( ) i

f

MxTMTTRN

µ = 'A M Tλ=-

100!

I AA ePI×

= ×



Transformer Bushing as Class A Item • 115 kV transformer bushings are investigated. • The data of 120 bushings from 40 transformers rating 115/22 kV, 25 MVA 1,890 unit-

years. • The design lifetime 25 years. • 5 failure records within 10 years time interval. • If the need for availability of items in stock is 99% of service level, the number of 115 kV

bushing in stock should be:

Normal distribution technique: • The number of bushing in the

stock is 2.2 or 3.

Poisson distribution technique • The 115 kV bushing should

be kept in stock 3 units per year for 99% stock reliability.



Surge Arrester as Class A Item

• 115 kV surge arrester are investigated. • The data from 120 arresters rating 115 kV (for transformer 25 MVA) 2,350 unit-years. • 2 failure records occurring within 10 years. • The design lifetime 25 years. • If the need for availability of items in stock is 99% of service level, the number of 115 kV

arresters kept in stock should be:

Normal distribution technique: • The number of arrester in the stock

is 1.2 or 2.

Poisson distribution technique • The 115 kV arrester should be

kept in stock 2 units per year for 99% stock reliability.



Number of bushing per year for various transformer ratings.



Conclusion Risk-based maintenance has been developed by combining condition and importance.

The spare part management was applied by classifying transformer component into

ABC classes.

The statistical analysis was performed to find the optimum number of class A item. It is

clearly seen that only a few numbers of bushings and arresters should be kept in stock

each year

The computerized web-application program is developed to facilitate the maintenance

tasks.

The effective maintenance tasks can be setup, resulting in high availability, low failure

risk, lower overall maintenance costs and ability to extend the useful lifetime.



Thank you for your attention

Presenter

Presentation Notes

2 thanapong asset mgmt tr

Documents

mva transformer

failure records transformer

management conclusion

good power quality

maintenance costs

preventive maintenance

component condition

known condition