parameter estimation for diagnosis and … estimation for diagnosis and optimization in power plants...

© ABB Power Technology Systems-1-ma

Para

met

er E

stim

atio

n fo

r Dia

gnos

is a

nd

Opt

imiz

atio

n in

Po

wer

Pla

nts

Pow

erG

en E

urop

eM

ilano

, Jun

e 20

05M

arc

Ant

oine

AB

B P

ower

Tec

hnol

ogy

Sys

tem

s


Intr

oduc

tion

Goa

l of p

aram

eter

est

imat

ion:

Gen

erat

e re

al-ti

me

estim

atio

n of

pla

nt p

aram

eter

san

d in

tern

al s

yste

m s

tate

sfo

r con

trol a

nd o

ptim

izat

ion

purp

oses

“Sta

tes"

= ti

me

vary

ing

mag

nitu

des

(e.g

. tem

pera

ture

s or

pre

ssur

es),

phys

ical

co

nsta

nts

(e.g

. mas

ses

or a

reas

), fu

nctio

ns o

f oth

er p

lant

sta

tes

(e.g

. effi

cien

cies

, ch

arac

teris

tics)

, etc

.

Tech

nolo

gy

Kal

man

Filt

er:

Predictm

odel

sta

tes

and

outp

uts

give

n th

e pr

evio

us e

stim

atio

n of

the

stat

es

Estim

ate

the

mod

el s

tate

s by

correcting

stat

e pr

edic

tion

acco

rdin

g th

e la

test

m

easu

rem

ent


Para

met

er E

stim

atio

n –

Boi

lers

Deg

rada

tion

of h

eat t

rans

fer d

ueto

soo

t dep

osits

Est

imat

e su

rface

effe

ctiv

enes

s fa

ctor

s, fu

rnac

e fo

ulin

g fa

ctor

s, s

lagg

ing,

etc

.


Para

met

er E

stim

atio

n–

Cem

ent K

iln

Vol

atile

s: S

ulph

ur, C

hlor

ine,

etc

.

Vola

tile

Vapo

uris

atio

n

Vapo

ur T

rans

port

atio

n

Solid

Tra

nspo

rtat

ion

Con

dens

atio

n

Vola

tility

Los

s

Unk

now

n pa

ram

eter

sto

be

estim

ated

-C(t)

Mea

sure

d Vo

latil

ity(S

tate

)


Para

met

er E

stim

atio

n–

GT

The

cons

umed

ene

rgy

bya

GT

com

pres

sor t

ypic

ally

ac

coun

ts fo

r50%

of t

he fu

el.

Com

pres

sor F

oulin

gdu

eto

pol

len,

dus

t, hy

droc

arbo

n ae

roso

ls, s

alt..

.

Turb

ine

Ero

sion

due

to a

bbra

sive

rem

oval

of b

lade

mat

eria

l.

Turb

ine

Foul

ing

due

accu

mul

atin

g co

mbu

stio

n re

sidu

als

on b

lade

s.

Fore

ign

Obj

ect D

amag

e (F

OD

) due

to d

etac

hed

parts

, com

pone

nts

brea

kdow

n.

Cha

nges

Flo

w c

apac

ity, T

empe

ratu

res,

Pre

ssur

es, E

ffici

ency

, Pow

er, C

oolin

g…G

T co

ntro

ller w

ill tr

y to

com

pens

ate

(e.g

. VIG

V; T

AT…

)C

ompr

esso

r Was

hing

to p

artia

lly re

cove

r fro

m C

ompr

esso

r Fou

ling.


Exam

ples

aro

und

the

GT

Para

met

er E

stim

atio

nW

ash

Opt

imiz

erG

as P

ath

Dia

gnos

is


Exam

ple

of E

stim

atio

n R

esul

t

0.70

0.75

0.80

0.85

0.90

0.95

78

910

1112

1314

1516

17

Pre

ssur

e R

atioBe

fore

Out

age

Afte

r Out

age

Diff

eren

t cor

rect

ed e

stim

ates

of e

ffici

ency

vs.

pre

ssur

e ra

tio.

The

uppe

r cur

ve w

as e

stim

ated

one

wee

k af

ter t

he lo

wer

cur

ve.

Dur

ing

the

wee

k be

twee

n th

e tw

o es

timat

es, t

he p

lant

was

dow

n fo

r mai

nten

ance

. Th

e es

timat

ed im

prov

emen

t in

effic

ienc

y is

abo

ut 2

% o

ver t

he e

ntire

rang

e.


Com

pres

sor W

ash

Opt

imiz

er

Feat

ures

Iden

tifie

s co

mpr

esso

r map

s ba

sed

on

para

met

er e

stim

atio

nD

eter

min

es o

ptim

al w

ashi

ng s

ched

ule

base

d on

cos

ts a

nd M

ixed

Log

ical

D

ynam

ics

(MLD

)P

ossi

ble

to d

efin

e an

d m

odify

op

timiz

atio

n co

nstra

ints

Ben

efits

Dec

isio

n-su

ppor

t too

l

Sig

nific

ant O

&M

Cos

t Sav

ings

Pre

dict

ive

inst

ead

of P

reve

ntiv

eG

R: N

orm

al s

tate

, no

was

hing

BL: O

nlin

e w

ashi

ngR

E: O

fflin

e w

ashi

ngYE

: Idl

e st

ate

WH

: No

cons

train

t


Com

pres

sor W

ash

Opt

imiz

er

Met

hod

Mod

el re

late

s st

ate

varia

bles

(m, p

,...)

to m

easu

red

varia

bles

and

unk

now

ns (η

,...)

Est

imat

or fo

r tim

e-va

ryin

g un

know

n pa

ram

eter

s

Effi

cien

cyD

egra

datio

n M

odel

to b

e lin

ked

with

an O

ptim

izer

eng

ine

Hyb

rid D

ynam

ic M

odel

link

s th

e ph

ysic

al m

odel

and

the

econ

omic

mod

el („

hybr

id“

beca

use

of B

oole

an d

ecis

ion

varia

bles

)

Per

form

ance

mod

elM

easu

red

data

Para

met

er E

stim

atio

nw

ith K

alm

anFi

lter

Deg

rada

tion

mod

elFu

el c

ost s

avin

gs

Hyb

rid D

ynam

ic M

odel

Opt

imiz

atio

n

Bou

ndar

y co

ndito

ns


Con

tinuo

usVa

riabl

es

Act

ual d

egra

datio

n vs

. tim

e

Hel

pva

riabl

e

Hel

pva

riabl

e

Non

-reco

vera

ble

degr

adat

ion

η : A

ctua

l deg

rada

tion

η 2: N

on-re

cove

rabl

e de

grad

atio

nα

: D

egra

datio

n ra

te fo

rηα

2: D

egra

datio

n ra

te fo

rη2


Intr

oduc

tion

of B

oole

anVa

riabl

esδ 1

δ 2δ 3

δ 4δ 5

δ 6η

η 2D

ay 0

αz 1

α2

z 2z 3

z 4

δ 1δ 2

δ 3δ 4

δ 5δ 6

ηη 2

Day

1α

z 1α

2z 2

z 3z 4

δ 1δ 2

δ 3δ 4

δ 5δ 6

η 2D

ay 2

δ 1: N

orm

al s

tate

, no

was

hing

δ 2: O

nlin

e w

ashi

ngδ 3

: Offl

ine

was

hing

δ 4: I

dle

stat

e, n

o po

wer

gen

erat

edδ 5

: hel

p va

riabl

e fo

r α (d

egra

datio

n ra

te fo

r η)

δ 6: h

elp

varia

ble

for α

2 (d

egra

datio

nra

te fo

rη2)

η : A

ctua

l deg

rada

tion

η 2: N

on-re

cove

rabl

e de

grad

atio

n

z 1-z

4: V

aria

bles

for o

bjec

tive

func

tion

(to b

e de

fined

)

ηα

z 1α

2z 2

z 3z 4


Rec

eedi

ng H

oriz

on C

ontr

ol

δ 1δ 2

δ 3δ 4

δ 5δ 6

ηα

α2

z 1z 2

z 3z 4

Day

0η 2

δ 1δ 2

δ 3δ 4

δ 5δ 6

ηα

α2

z 1z 2

z 3z 4

η 2

δ 1δ 2

δ 3δ 4

δ 5δ 6

ηα

α2

z 1z 2

z 3z 4

η 2

δ 1δ 2

δ 3δ 4

δ 5δ 6

ηα

α2

z 1z 2

z 3z 4

η 2

δ 1δ 2

δ 3δ 4

δ 5δ 6

ηα

α2

z 1z 2

z 3z 4

η 2

Day

1

Day

2

Day

3

Day

N


Com

pres

sor W

ash

Opt

imiz

er–

Res

ults

No

was

hing

Non

-rec

over

able

deg

rada

tion

Opt

imiz

ed s

olut

ion

0

Eve

ry w

eek

-46%

Opt

imal

-22%

+60%

Add

ition

al C

osts

*

Eve

ry D

ayN

o W

ashi

ngW

ashi

ng C

ycle

* nor

mal

ized


Dev

elop

men

t of G

T D

iagn

ostic

s

Mea

sure

men

tM

onito

ring

Faul

t Tre

eFa

ult M

atrix

Qua

litat

ive

Faul

t D

iagn

ostic

sQ

ualit

ativ

e Fa

ult

Dia

gnos

ticsQua

ntita

tive

Faul

t D

iagn

ostic

sQ

uant

itativ

e Fa

ult

Dia

gnos

tics

Mai

nten

ance

Str

ateg

y

GT Gas Path Diagno

Prog

ress

ive

Prog

ress

ive

Mai

nten

ance

Str

ateg

y

sis Mon

itorin

gM

onito

ring

GPD

GPA

Tren

ding


GT

Gas

Pat

h D

iagn

osis

Sym

ptom

s ca

nnot

trac

e ba

ck to

con

ditio

n un

equi

voca

lly:

Unc

erta

inty

in c

ause

sM

easu

rem

ents

are

inex

act,

corr

upte

d w

ith n

oise

:U

ncer

tain

ty in

obs

erva

tions

⇒P

RO

BA

BIL

ISTI

C IN

FER

EN

CE

Est

imat

or(D

EKF**

)tak

es n

oise

of m

easu

rem

ents

and

of

proc

ess

into

acc

ount

(tim

e-va

ryin

g he

alth

par

amet

ers)

Det

erm

ines

cov

aria

nces

and

cal

cula

tes

join

t pdf

*fo

r the

es

timat

ed h

ealth

par

amet

ers

Pro

babi

listic

Cla

ssifi

erqu

antif

ies

com

pone

nt’s

leve

l of

degr

adat

ion

Pre

pro

cess

ing

Ga

s P

ath

An

aly

sis

Ga

s P

ath

Dia

gn

osi

s

Pre

pro

ce

ss

ed

me

as

ure

me

nt

sig

na

l se

t

He

alt

h p

ara

me

ter

Eq

uip

me

nt

fau

lt lik

elih

oo

d

Me

asu

rem

en

t s

ign

al

set

*Pro

babi

lity

Dis

tribu

tion

Func

tion

** D

iscr

ete

Exte

nded

Kal

man

Filt

er


Dis

cret

e(E

xten

ded)

Kal

man

Filt

er

The

ingr

edie

nts:

A d

iscr

ete

proc

ess

mod

elC

hang

e in

sta

te o

vert

ime

Diff

eren

ce e

quat

ion

A d

iscr

ete

mea

sure

men

t mod

elR

elat

ions

hip

betw

een

stat

ean

d m

easu

rem

ent

Mod

el P

aram

eter

sP

roce

ss n

oise

char

acte

ristic

s

Mea

sure

men

t noi

sech

arac

teris

tics

fh

u(k)

D

x(k-

1)

y(k)

+z(k)

x(k)

v(k)

u: in

put s

igna

lx:

sta

te v

ecto

rv:

pro

cess

noi

sez:

mea

sure

men

t noi

sef:

mod

el fu

nctio

nh:

out

put f

unct

ion


Faul

t Sym

ptom

Mod

el P

aram

eter

sFa

ult-c

ondi

tiona

l den

sity

is

para

met

rized

by:

subs

et o

f sym

ptom

ele

men

ts j

impa

cted

by

faul

tF i

expe

cted

deg

rada

tion

(in c

ase

of fa

ultF

i)

→“c

loud

cen

ter”

stan

dard

dev

iatio

n of

deg

rada

tion

→“c

loud

wid

th”

corr

elat

ion

of d

egra

datio

ns

→“c

loud

orie

ntat

ion”

)(

iFp

δ

σ

µ

ρ

)|

(Ei

iF

δ=

)|

(V

ar,

ij

ji

Fδ

=

1|

|),

|,

(C

orr

,,

≤=

jki

ik

jjk

iF

ρδ

δ

-0.0

8-0

.06

-0.0

4-0

.02

00.

020.

040.

060.

080

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Turb

ineF

oulin

gTu

rbin

eEro

sion

Turb

ineF

OD

Sym

ptom

S1

Symptom S2

Opt

imal


GT

Gas

Pat

h D

iagn

osis

-Ex

ampl

e

Mon

itore

d ev

olut

ion

of s

ympt

oms,

cod

ed b

y tra

ject

ory:

Pos

terio

r pro

babi

lity,

cod

ed b

yco

lour

map

:)

|(

δiF

P)

(tδ

Feat

ures

Iden

tifie

s an

d qu

antif

ies

sing

lean

d m

ultip

le p

hysi

cal f

aults

(foul

ing,

ero

sion

, FO

D, e

tc.)

Rea

l-tim

e in

form

atio

n on

GT

heal

thH

elps

avo

idin

g un

nece

ssar

y tri

ps

Ben

efits

Incr

ease

s tru

st in

inst

rum

enta

tion

Red

uces

dow

ntim

eE

xten

ds M

aint

enan

ce In

terv

alP

rovi

des

early

pro

blem

Rec

ogni

tion

Mov

ie


Con

clus

ions

Para

met

er

estim

atio

nis

a

tech

niqu

e th

at

can

be

used

fo

r id

entif

icat

ion

of

com

plex

sy

stem

s,

in

parti

cula

r fo

r co

ntro

l, di

agno

sis,

and

mai

nten

ance

opt

imiz

atio

n.

Com

bine

d w

ith M

odel

Pre

dict

ive

Con

trol(

MP

C) t

his

can

be u

sed

for

econ

omic

opt

imiz

atio

n(e

.g. c

ompr

esso

r was

h op

timiz

er).

The

sam

e pa

ram

eter

est

imat

ion

tech

niqu

e ca

n be

ext

ende

d fo

r eq

uipm

ent d

iagn

osis

.

Equ

ipm

ent

faul

ts a

re s

peci

fied

by a

con

fiden

ce d

omai

n an

d th

e pr

obab

ilist

ic c

lass

ifier

allo

ws

quan

tifyi

ng a

com

pone

nt's

“le

vel o

f de

grad

atio

n".

parameter estimation for diagnosis and … estimation for diagnosis and optimization in power plants...

Documents