Decision-making process for substation renovation …ewh.ieee.org/cmte/substations/scm0/Kansas...

RWE Transportnetz Strom 08-2008 PAGE 1

Decision-making process for substation renovation and equipment

end of life assessment

C. Neumann (Germany)

2009 IEEE Substations Committee Meeting, Kansas City

RWE Transportnetz Strom 04.2009 PAGE 2

Decision-making process for substation renovation and equipment end of life assessment

1.Introduction (German EHV grid)

2.Basic methodology

3.Ascertainment of actual condition

4.Determination of other c - parameters

5.Assessment of end of service life

6.Combination of parameters for decision making

7.Conclusion


Introduction

Fundamental changes in the regulatory framework of the electricity market in the last decade

Intensified efforts of the grid operators for an optimized utilization of their networks with respect to technical and economical aspects

At the same time the grid operators have to assure a sufficient power quality & reliability

Substations are the nodes of the grid substantially affecting the reliability & availability of the grid in total

Substations represent an essential part of the grid assets

Of particular interest with regard to optimization of the grid costs, i. e. investments & operational expenditure – CAPEX & OPEX

⇒ Methodology and decision making process for substation renovation and equipment end of life assessment


RWE

EON

VET

EnBW

13193038Share load [%]**

1,9366,7005,4005,200Network length 380 kV [km]

1,7212,8655,3006,100Network length220 kV [km]

76k.A.138175Annual transmission [TWh]

34.6 109.0139.473.1Served area [1000 km²]*

EnBWVETE.ONRWE

* in Germany** Renewable Energy Act load compensation 2005

German EHV grid Operational areas of German Transmission System Operators


RWE

EON

VET

EnBW

RWE

220 kV transmission lines




RWE



90,100 MVA

121110 kV substations

145220 kV transformer

235380 kV transformer



6,100 km220 kV circuits

5,200 km380 kV circuits



Information for decision making process

AM ⇔ qualified information of the system and the equipment installed for the decision making process.

In case of larger population difficult to provide key information manually; therefore reasonable

– application of data based systems

– development of algorithms

Algorithms and method for – determining near-term action and annual business planning

– forecasting the technical and financial effect due to system ageing

Approach based on condition and importance


Assessment of condition and importance parameters (1)

Assessment of condition parameters

1. Equipment level regarding equipment specific condition parameters, assessment by school marks

static condition quantities: technology, type related service experience (e. g. after sales service quality, maintenance costs), individual failure rates

dynamic condition quantities: age of equipment, individual condition ascertained by inspection and condition checks, interval to next planned maintenance activity

2. Equipment condition parameters accumulated on bay level, weighted according the value of different equipment

3. Assessment on station level → bay condition parameters and on system level → station condition parameters, weighted average mean value


Assessment of condition parameters on different levels

Subst. A..Z

bay 1

Subst. A Subst. B

circuit breaker disconn. arresterprotect.

transf.

inst. transf.

infrastruct.UPSsubst. contr.bay 2

infrastructureEquimentlevel

Baylevel

Substationlevel

Systemlevel

Condition assessment

Condition parameters

staticquantities

dynamicquantities


Assessment of condition and importance parameters (2)

Assessment of importance parameters

– Reliability analysis of the different substations

– Derived from short circuit power of the station in question

weighted by a factor reflecting the relevance of the station in the system

Condition and importance parameters normalised to 100

For reliability analysis failure rates to be

regarded. However, failure rates still considered with

condition parameters


replacement necessary

need for action to be checked

maintenance accor-ding to strategy

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100small <<< importance >>> high

good

<<<

con

ditio

n>>

> ba

d

380 kV220 kV110 kV

Condition and importance on equipment level Example: Circuit breakers


0

20

40

60

80

30 40 50 60 70 80importance

cond

ition

C totC priC secC pri >50C sec >50

Condition and importance parameters of a population of 380 kV stations

BC

A


0

10

20

30

40

50

60

401 402 403 404 407 409 410 411

bay

cond

ition

0

10

20

30

40

50

60

401 402 403 404 407 409 410 411

bay

cond

ition

Condition on station level and bay level – condition above 50 –

0

20

40

60

80

100

TE1 TE1 TE1 LS WI WU TD2 ATD

equipmentco

nditi

on



1. Introduction

2. Basic methodology

3. Ascertainment of actual condition

4. Determination of other c - parameters

5. Assessment of end of service life

6. Combination of parameters for decision making

7. Conclusion


Qualified assessment of actual condition of switching equipment

Qualified condition assessment needs knowledge of the equipment under consideration and the physical background ⇒ experienced and highly skilled personnel.

In case of a large amount of different pieces of equipment and of different types ⇒ high expenditure for training of specialised personnel

⇒ New approach: Application of an “automated, user instructed and data based inspection and diagnosis system” (ADS)

ADS system in use for HV CBs since several years, for DSs under development


Basic design of “ADS“ system(automated, user instructed and data based inspection and diagnosis)

SF6-Check

Contact travel

Contact resistancestatic & dynamic

diagnosis boxprocess controldata acquisition

data transfer

diagnosis plugcontrol & supervision ofswitching process & driveUser assistance

Maintenance expert may control and supervise the process by remote access UMTS

Im

Um 1), 2), 3) adaptive sensors

1)

3)

2)


ADS Features

Diagnostic box → four inputs to record different diagnostic quantities

Sequence of the inspection process is automated:

After input of the general data of the breaker to be inspected →maintenance personnel instructed what actions are to be done

All quantities are measured & analysed automatically, all results are stored in a data base

⇒ Reliable, objective & qualified assessment of the actual condition of the equipment under consideration

Depending on the measuring results and the condition check →information, if and what corrective measures have to be taken.


Results of ADS diagnosis on a 245 kV CB with two interrupter units

20

40

60

80

100

130

20

40

60

80

100

130

24

6810

12

1416

20[mΩ]

24

6810

12

1416

20[mΩ

0 00 10 20 30 40 50 60 70 80ms

0 00 10 20 30 40 50 60 70 80ms

tripping coil current

0.0

0.4

0.8

1.2

1.6

2.0[A] [mm]

switching off process

resistanceunit 1 & 2

0.0

0.4

0.8

1.2

1.6

2.0[A]

0.0

0.4

0.8

1.2

1.6

2.0[A]

contact travel

20 25 30 3520 25 30 35

[mm]

75 µΩ


Data recorded and analysed by a single shot

The following data can be recorded and analysed by a single shot:

Static and dynamic contact resistance

Operating (making or breaking) time of the main and the auxiliary contacts

Contact travel, i. e. velocity and damping

Current and time characteristic of the tripping coil

In case of an hydraulic drive pressure drop of the hydraulic pressure

All measured results are recorded and analyzed automatically andafterwards stored in a data base.


1. Introduction



4. Determination of other c – parameters

Maintenance,

after sales service



7. Conclusion



Average overall maintenance costs of 420 kV CBs per CB-year

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6major eventminor event

visual inspectionspecial measure

planed maint. condition acquisition

Ave

rage

over

allm

a int

e nan

cec o

stpe

rCB

-yea

r (in

%o f

new

CB

cost

)

A80

s

B80

s

C63

s

D63

s

E63

s

F63s

G63

s

H63

s

I63o


Assessment of after sales service quality (CBs)

Man

pow

er fo

r in

stal

latio

n

Inst

alla

tion

time

feas

ibilit

y of

CB

reus

e

Feed

back

with

m

anuf

actu

rer

Stuf

f ava

ilabi

lity

for f

ailu

re re

mov

al

Asse

mbl

y tim

e in

cas

e of

failu

re

Avai

labi

lity

of

spar

e pa

rts

Stuf

f ava

ilabi

lity

for m

aint

enan

ce

Qua

lity

of

mai

nten

ance

C

ost o

f mai

nten

e-

nanc

e pa

ckag

e Ti

me

of u

nava

ila-

bilit

y du

ring

mai

nten

ance

A

sses

smen

t re

sults

Weight [%] 1.8 8.0 8.0 15.6 8.0 11.6 13.5 5.5 9.8 15.9 3.3 100 Range 1-6 1-6 1-6 1-6 1-6 1-6 1-6 1-6 1-6 1-6 1-6 0-100

Type A80s 3 5 1 2 1 3 1 1 1 3 3 21.6Type B80s 5 5 6 5 5 5 5 5 5 4 5 78.6Type C63s 2 2 1 2 1 2 1 1 1 2 2 10.8Type D63s 5 5 6 2 1 3 1 1 1 3 3 30.6Type E63s 3 5 1 3 3 2 2 3 3 2 2 31.7Type F63s 3 5 1 5 5 2 2 3 5 1 1 42.5Type G63s 5 5 1 5 5 2 2 3 5 1 1 43.2Type H63s 3 5 1 5 5 3 3 3 5 3 4 53.8Type I63o 5 5 6 5 5 5 3 3 5 6 6 76.9

Weighting by a decision matrix

Best service 0Worst service 100


1. Introduction



4. Determination of other c - parameters



7. Conclusion



Average failure rates of 420 kV CBs

0

5

10

15

20

25

30

35

40

A80s

B80 s

C6 3

s

D6 3

s

E63 s

F63s

G6 3

s

H6 3

s

I63o

Av e

rage

f ailu

rera

tepe

r100

CB

- yea

r s


Ageing given by related failure frequency (RFF)

Confidence (98%)Failure event years of service

Example of a certain 420 kV CB type

0102030405060708090

100

0 4 8 12 16 20 24years of service

at failure occurrence

RFF

[%]

0

20

40

60

80

100

120

year

s of

ser

vice

Typical ageing process

– Linear loss of basic substance

– Exponentially increasing related failure frequency

– Worse service experience for older equipment

RFF: Failures per year related to 100 CB years [%]


Related failure Frequency (RFF) of 420 kV CBs

Failure Frequency (RFF) of 420 kV circuit breakers with 95% confidence interval

RFF

[ % ]/

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32Age (years)

34 36 38 40

Confidenceinterval

RFF (minor)

RFF (major)

Lin. Regression of RFF (major)

Exp. Regression of RFF (minor)

Exp. Regression of RFF (all CBs)

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40Age (years)

Confidenceinterval

RFF (minor)RFF (major)

Lin. Regression of RFF (major)

Lin. Regression of RFF (minor)

Exp. Regression of RFF (all CBs)

RF F

[ % ]/


0

20

40

60

80

100

120

140

160

180

200

0 5 10 15 20 25 30 35 40 45 50 55Service time [a]

utili

zatio

n fa

ctor

[%] *

)

End of life prognosis due to knowledge of the basic ageing process

after 20 a

*) utilization factor => expected life related to nominal service life

end of servicelive earlier

end of servicelive later

after 40 a

CBs

⇒ In case of accelerated ageing renovation is brought forward, in case of decelerated postponed


0

20

40

60

80

100

120

140

160

180

200

0 5 10 15 20 25 30 35 40 45 50 55Service time [a]

utili

zatio

n fa

ctor

[%] *

)

End of life prognosis due to knowledge of the basic ageing process

after 20 a

*) utilization factor => expected life related to nominal service life

after 40 a

CBs

DSsarresters


End of service life assessment of 380 kV substations*(equipment with impermissible utilization factor will be exchanged)

Zukun

Zense

Witte

Weste

Weiße

Wehre

Walsu

Vöhri

Verla

Utfor

Urber

Unna

Uentr

Uchte

Tieng

Siers

Secht

Sankt

RoxelRosen

Romme

Rath

Polsu

Paffe

OpladOhlig Oberz

Nordl

Niede

Niede

Nehde

Mitte

Meppe

Menge

Meiti

Lippb

LindeLimbu

Liess

Leupo

Kusen

Krupp

Krift

KobleKnaps

Karna

Hülle

Hohen

Hatti

Hanek

Halfe

Gunde

Grona

Gerst

Ennig

Eiber

Detti

Dauer

Dahle

Büsch

Bürst

Bisch

Arpe

Arpe

Bisch

Bürst

Büsch

Dahle

Dauer

Detti

Eiber

Ennig

Gerst

Grona

GundeHalfe

Hanek

Hatti

Hohen

Hülle

Karna

KnapsKoble

Krift

Krupp

Kusen

Leupo

Liess

Limbu

Linde

Lippb

Meiti

MengeMeppe

Mitte

Nehde

Niede

Niede

Nordl

Oberz

Ohlig

Oplad

Paffe

Polsu

Rath

Romme

Rosen

Roxel

Sankt

SechtSiers

Tieng

Uchte

Uentr

Unna

Urber

Utfor

Verla

Vöhri

WalsuWehre

Weiße

Weste

Witte

Zense

Zukun

Arpe

Bisch

Bürst

Büsch

Dahle

Dauer

Detti

EiberEnnig

Gerst

Grona

Gunde

Halfe

Hanek

Hatti

Hohen

Hülle

Karna

Knaps

Koble

Krift

Krupp

KusenLeupo

Liess

Limbu

Linde

Lippb

Meiti

Menge

Meppe

Mitte

Nehde

Niede

Niede

Nordl

Oberz

Ohlig

Oplad

Paffe

Polsu

Rath

Romme

Rosen

Roxel

Sankt

SechtSiers

Tieng

Uchte

Uentr

Unna

Urber

Utfor

Verla

Vöhri

Walsu

Wehre

Weiße

Weste

Witte

Zense

Zukun

ZukunZense

Witte

Weste

Weiße

Wehre

WalsuVöhri

Verla

Utfor Urber

Unna

Uentr

Uchte

Tieng

Siers

Secht

SanktRoxel

RosenRomme

Rath

Polsu

Paffe

Oplad

Ohlig

Oberz

Nordl

Niede

NiedeNehde

Mitte

Meppe

Menge

Meiti

LippbLindeLimbu

Liess

Leupo

Kusen

Krupp

Krift Koble

Knaps

Karna

Hülle

Hohen

Hatti

Hanek

Halfe

Gunde

GronaGerst

EnnigEiber

Detti DauerDahle

Büsch

BürstBisch

Arpe

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100small<<< importance >>> high

smal

l <<<

>>>

high

actual + 10 a+ 5 a + 20 a

utili

zatio

n fa

ctor

[%] *

)


Conclusion

For support of the decision making process for substation renovation the asset management needs simple, but technically justified and effective method for assessment

–of actual conditions of the equipment

–end of equipment life

Methods and algorithms described provide qualified information

– for determining near-term action and annual business planning

–also for forecasting the technical and financial effect due to system ageing.


Thanks for Your attention!

Decision-making process for substation renovation and equipment end of life

assessment

Date post:	19-Aug-2018
Category:	Documents
Upload:	dinhdung
View:	214 times
Download:	0 times

Decision-making process for substation renovation …ewh.ieee.org/cmte/substations/scm0/Kansas...

Documents