topological methodology

8/7/2019 Topological Methodology

1/11

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ethodology

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1/11

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2/11

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Fig. 2

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2/11

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he plant staly solving pue to difficu

es a lot of p control. Pually carrie

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llowing asp

ialization; it

etter result

ased vision

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ize the pro

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3/11

3/11

Maintenance activity prioritization, Overall plant status provided in real time.

IT System Project

Development of the project consists of three different steps:

a) Preparation of the reference database and the automatic feeding structure b) Development of the analysis tools and interfaces c) Development of the real time data processor engine

Who feeds the information to the system?

Plants usually record all the process parameters using an electronic format; sampling

frequency from 0.2 Hz for the oldest plant to 2 5 Hz for the modern ones. The format of the

stored data is very simple: time, parameter name, value.

Considering that a plant has from 1000 to 5000 recorded parameters, these ones represent a

huge amount of available information.

The only data required are:

Recorded process data Feedback information about the performed maintenance activities

How the recorded information is useful for the envisaged scope

Conceptually, the recorded information represents the complete description of the universe

plant. Variations in the values of the parameters represent the description of the plant

evolution. It means that a prolonged and in depth analysis of the variations in parameters and

the relation among them allows us to obtain a complete written picture of the plant status

and evolution.

Theoretically, if the available information set is complete, the plant status at the next instant

is perfectly foreseeable within a reduced error band because all the parameters that could

affect the plant behaviour are known. The possible error has to be investigated on the

parameters

that

are

external

to

the

plant

and

so,

not

perfectly

foreseeable.

The recorded information, integrated by information manually gathered and inputted (e.g.

maintenance information), is enough for the envisaged scope.

Plant data base description

The data received from the plant are processed in order to obtain the information in a useful

form. A knowledge data base is required in order to correctly process the data flux. In that

data base the following data are coded:


4/11

Qual

Quali

valuereal

syste

Data

The

the tout

view

in tw

In thbetwprocpara

Instrumethe precclass conEquipmeFlows strand instrOperatintarget paSymmetrprocess cTarget st

ity Class

ty class is a

s of each pralue for ea

m is heuristi

Processing

roposed syraditional scy objectivewithout touo different

Not ChroChronolo

e first case,

een each siss makes

eters. A ex

nts and infoision class ocept is discunt and lines

ucture descuments;

g conditionrameters;

y and congonditions;

ructure whe

special con

ocessed parch parametcally adjusti

stem philosheme: time

and predeching the oays:

nologically

gically

not chronolgle value available, fo

ample is rep

rmation souf each instrssed later);catalog;

ribing the pr

describing

uency catal

re the struct

cept, introd

ameter. Praer (Fig. 3).

ng in functio

Fig.

phy is to pparameter/fined algoriiginal infor

ogically, thed the corre

r each parorted in Fig

4/11

rces catalogment and

ocess flows

the differe

log describi

ure of the t

uced in ord

ctically, eacuality class

n of the rec

3 Quality Cla

rovide the

value. This sthms with t

ation mea

values are

sponding vameter, the

re 4.

with referehe quality c

and their r

t operatin

g the equi

rgets is defi

r to obtain

quality cla range is a

eived input.

sses

lant informtep requirehe scope toing. Data fe

processed ilue of the rnumerical

ce to the rlass assign

lations with

conditions

ment and

ned.

a numeric

s value repdynamic as

ation in diff a pre proc offering a

d from the

order to oferred targ

relation bet

lated equipent rules (q

equipment

and definin

lines with s

valuation f

resents a raignation th

erent formsss activity c

different poplant is proc

btain the ret parameteween targe

ment;

uality

, lines

g the

imilar

r the

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t the

from

arried

int of

essed

lation

. This

t and


5/11

The s

Confi

the ea sinPlant

Even

Evenstartcorredura

Even

econd proc

gurations: bntire plant

gle key and

Power Out

ts: For each

t is open. Ad. An eve

sponding tion, amplit

t Frequency

ingle Fuel

ss, Chronol

y assigning

onfiguratiothey do ref ut) (Fig. 5).

parameter

event is unnt is clos

its nominde (in qual

Analysis is r

300350

F u e l C h

285

290

295

300

305

310

l Channel

Fig. 4

gically, is di

to each para for each prrence to th

Fig. 5

uality class

ivocally ided when tal value. Fity class), ti

ported in Fi

10015020

050

a n n e l

ontributio

5/11

ot Chronologi

vided in tw

meter valuocessed inse main targ

Plant Configu

that has betified by the parametr each eve passed f

igure 6.

050 3 0 7

to Prim

cal result

sections:

its quality ctant. The reet paramete

rations

en changed

parameterer value rnt, signific

rom the pa

3 0 6 .5

3 0 6 3 0 5 .

PHT

ry Heat Tr

lass code is

orded data

r for that in

from the prname and tturns to

nt informat one, etc.

3 0 5 3 0 4 .5

emperature

C

285

290

295

300

305

310

ansport S

possible to rare recognistant (usual

evious instahe time whthe quality

tion is collA example

Fuel Rod Channel Temperature C

stem (PH

ecord

ed as

ly the

nt, an

n it is

class

cted:

f the

)


6/11

Next

All thValu

Next

resul

esti

Instant

e described calculation

value is an

t the value

ated value

estimatioestimatioone.

500

0

500

1000

1500

2000

2500

EventsNum

ber

processes

basis.

utomatic coof each pa

ange as a re

n based on

n based on

Fig. 6 E

re carried o

mputation (rameter ex

sult of a do

the previouthe behavio

Fig. 7 N

Stea

Even

6/11

vent Frequenc

ut either o

Fig. 7) on Nected at t

ble calculat

trend of thr of all proc

ext Instant co

Gener

t Duratio

y Analysis

the real va

ural Compue next ins

ion process:

processed

essed para

putation

tor Lev

(hh:mm

lues basis, e

tation basis

ant. The pr

parameter;

eter exclud

l

:ss)

ither on th

that providocess obtai

ing the proc

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s as a

ns an

essed


7/11

The

real

exac

The

real

what

Time

The

Perf

As rebut tdefinthe

suffevaria

rocessing oalue. In thisly happene

vents procealue. The e

happened.

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processed drmance Mo

ported, the

he informatied any resuleveral procrs a differetion; the dif

f the Next I way and wi

and what

ss for the Nent is open

That metho

ata availabinitoring but

set of valueon is still nt obtained f essed parat delay in

erence is a

nstant estimth whicheveas expecte

Fig. 8

ext Instant

once the qd allows us t

lity procure some other

s for each int correctly

rom the chreters has

is variationime Consta

Fig. 9 Ti

7/11

ated valuer, Time Leve

is available

Upper time le

alues is notality class oo obtain ea

s the starti very impor

stant contaligned on t

onological dnot an inst

that depent (Fig. 9).

e Constant c

is carried ol (discussed

and compa

el path

exactly the

f the expectly alert on t

g board foant informa

ins the infore time axis.

ata process.ntaneous eds on the

mputation

t in the salater) inforable (Fig. 8)

same as theed data rese potential

r the promtion is still

mation abo This situatiIn fact, the

ffect. Each

arameter t

e manner

ation abou.

one used f lts differen

failure.

ised Global

issing.

ut the plant

on does not

influence thtarget parahat produce

s the

what

r the

from

Plant

trend

make

rough

meter

d the


8/11

The

then

chro

Univ

The

paraconti10).

seveof dither

in

th

This

that

for cpossi

Time

A spdescarrivi

ime Constaapplied oologically p

cal Configu

tatement theters gath

nuously resor each valal different

ferent quali different v

universe

information

affect the pontrol, the

bilities:

Initial deAn exteperform

Coding

cial coding

iption of thng to the e

nt is autom the inpu

rocessed re

ration

at the uniered has tected, the

ue of the taplant configty classes of

lues of the

something

represents

lant perforevidence t

ign considenal environce

for the timee time for

nd of its ex

atically com data. Thiult in a perf

erse plant

o be backeConfigurati

rget parameurations couthe parameparameter t

ot

controlle

Fig. 10 Uni

an importaance. Consat some u

rations weremental va

axis descripeach plant

tended life.

8/11

puted on th procedure

ectly aligne

and its evold up. In ons obtaineter (usually

ld exist; theters submitarget for a

d

is

affectin

vocal configur

t tool in oridering that

ncontrolled

not complriable or a

tion has beinstant, staThis codin

e event tre allows us

manner in

ution are rerder to en

are submithe Plant Pse plant coned to the taingle config

g

the

target.

tion process

der to hunall the desivariable e

te

human f

en introducrting from

(Fig. 11) in

d basis andto obtain

erms of cau

resented bure that t

tted for furwer Outpufigurations

rget. A diffeuration app

t the uncogn parametists highlig

ctor is aff

d in order the plant cocludes vari

the correcall input

se effect.

y the value

is affirmatther analysi

at the first

re a combirent case ocear; it mean

ntrolled varers are subhts two dif

ecting the

o have a unmmissioninus levels o

ion is

alues

of the

ion is

s (Fig.

level)

ation

curs if

s that

iables

itted

erent

plant

ivocal

g and

time


9/11

descwee

The lare atime

unco

Time

receiaverintercodiman

The iperfo

Deve

Goal

that

proc

Thes

analallo

iption: life

, shift, hour,

evels includlso considercondition (

ntrolled vari

coding allved on highge value (oal. This vag. The dataer already

nformation

rm addition

Providinextend ttrend paProvidin to perfo

lopment of

of the desccannot be

ss, the real

data repr

zed by anal the definiti

ime (stand minute and

d in the tied as input

e.g. certainable is redu

ows perfor frequency

tained throlues reporti process on

escribed.

availability

al analysis o

a path for

e Event fuh (Fig. 8);

long term

rm an evalu

the Analysi

ibed data peachable u

and forecas

esent a co

ysis tools aon of:

rd or exte second.

Fig

e coding (parameter

days of

ed and mos

ing an adbasis (secough a dedicg is perforall the con

about whatn the data i

the expectction to a s

asis analysi

ation of mai

Tools

rocessing is

sing traditioed informat

tinuously

d interface

9/11

ded Plant

. 11 Time Co

onth, weekand they alleek, seasot of them c

ditional dads or fractted statistic

med througidered twe

happened

put level:

d trend oituation wh

on the par

ntenance a

to obtain a

nal methoion is very

pdated sta

s. These to

ife), year,

ing

, day of weow us to as

or shift).n be associa

ta processiions) reportal algorithm

all the levve time lev

during the

the lower

n the value

meters dev

d aging.

set of poinology. Coell identifie

tus of the

ls, directly

eason, mo

k, hour, miociate the

In this wated to a spe

g. The oris to the up) within the

ls considerls is carrie

long lead le

level inform is out of th

iation, allow

s of view osidering th

every time

plant, whic

onnected t

th, week,

nute, seconevent to a c

the numcific time le

ginal inforper time leconsidered

d within th out in the

vels allows

ation, perme expected

s us for ex

n the plant

innovative.

h can be f

o the data

ay of

, etc)

ertain

er of

el.

ation

el its

upper

time

same

us to

its to

upper

mple

status

data

rther

ases,


10/11

10/11

The best working point for each parameter depending on its target (example is reported in Figure 12). This evaluation is performed from the Not Chronological data

process. The condition really processed in the plant allows us to obtain the optimum

working point even if it is out of the processed values range, by analytical and neural

computation;

Fig. 12 Variable real working point

The early event alert is obtained from the prediction of the next value solved on the previous trend basis. Evidence of the difference between the foreseen value and the

real occurred one, highlights a discrepancy or abnormal condition. This fact is useful in

order to detect potential failures earlier. The next value existing range is obtained

through a double neural computation: the first on the time line basis and the second one based on the expected value within a certain parameter values set;

Modeling hypothetical process conditions produces heuristic models, which can be used to predict plant responses (Fig. 13). Configuration Data Base associated with the

Not Chronological process allows us to foresee the plant response in operative

conditions not previously tested;

Fig. 13 Variable Response Estimation

6 5 0 6 4 7

6 4 4 6 4 1

6 3 8 6 3 5

6 3 2 6 2 9

6 2 6 6 2 3

Gross Po

wer Output

MWe

158157.8157.615

7.4157.2157156

.8156.6156.4156.

2

F W T e m p e r a t u r e C

-100000 -100000

100000 100000300000 300000500000 500000700000 700000900000 900000

1.1e+06 1.1e+061.3e+06 1.3e+061.5e+06 1.5e+06

Repetitively

Repetitively

Feedwater TemperatureReal Working Point

FW Temperature vs Gross Power Output

63063463864

26466506546

58662666

G r o s s P o w e r O u t p u t M W e

1 5 8 1 5 7 .8

1 5 7 .6 1 5 7 .4

1 5 7 .2 1 5 7

1 5 6 .8 1 5 6 .6

1 5 6 .4 1 5 6 .2

FW Te

mpera

ture C-2500

-25000

02500

25005000

50007500

750010000

10000

Certitude Index

Certitude Index

Feedwater TemperatureWorking Range Projection

Feedwater Temperature vs Gross Power Output


11/11

11/11

Aging evaluation, preventive maintenance requirements and equipment performance losses can be reached by long term deviation analyzing the effect that a parameter

produces on its target, associating it with the history of performed work by the related

equipment and components.

Conclusions

This new methodology is under test at two Nuclear Power Plants, in Europe and out of Europe.

The first results are very comforting and positive, giving strength to continue with the tests

and the analysis of the data obtained. The system can become an automatic plant supervisor

capable of working methodically 24h/24h, to learn from the past and to continue working for

the complete plant life. The tool can also provide general plant tuning, improving the

operating conditions and producing economic benefits.

topological methodology

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