topological methodology
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8/7/2019 Topological Methodology
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Top
opti
M. T
Pref
Startbouninfor
Nordisci
Manplantelectunde
In thcolle
The
seve
The t
ological m
ization
glia, M. Za
ce
ing from thd to technimation it is
ally in the
line, group
DeficiencSituationSafety as
tools are n from a sperical, etc.) ar the contro
is conditionting all the
ffectivenesal factors:
SpecializSpecializSubjectivUpper m
raditional m
Upper m
observer
thod appli
ra, A. Peras
consideratal plant beossible to i
plant, takis of speciali
ies
at
the
Pls that can ppects.
ormally avacific point
nd by depal of the spec
, the globaprocessed r
of the tradi
d teams obd teams ca
e perceptionagement
ethodology
anagement
s;
ed to Nucl
so, F. Benve
ion that thehaviours, b
prove plan
ng into coed technici
ant
present
oduce defic
ilable, to cof view, usu
rtment (opeialized team
l plant visiosults from t
Fig. 1
tional appro
servation anability to u of the probapability to
highlights t
point of vi
1/11
lear Power
nuto
performan opportune
t performan
sideration
ns provide
status;
iencies in th
rrect or preally addressrations, ma and operat
n is deleghe various t
Traditional A
ach to the
d fast actioderstand thlem by the
quickly get
o different
w is alway
Station m
ces of a Nuly managince.
the informeedbacks o
e future;
ent deficieing the topiintenance,
s independ
ted only toams.
proach
lant Perfor
;
e interdiscieams mem
results from
issues:
s previously
onitoring
lear Power
all availab
tion relate various typ
cies; each
cs by disciplngineering,
ently from t
the upper
ance Moni
linary issue;ers;
the speciali
filtered by
nd perfor
Plant are dile plant tec
d to their
es of issues:
ool considelines (mechetc). Each
he others (Fi
manageme
oring depe
zed team re
the interm
ance
rectly
hnical
direct
rs the
nical,
ool is
g. 1).
nt by
ds on
orts.
diate
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Crea2), i.
Engi
In a
but
moniauto
Autobase,auto
The r
Intermedan unbia
ing a correc.:
Allow all
plant;
Improve
of deficieMinimizeunderstaAllow upMake ththat, wit
or due to
eering and
lant, the auhese data
toring is stillatic suppo
matically col directly an
atically pe
esult of the
IdentificaEarly failDesign pProcess pPlant beIntegrati
iate observed point of
t integrated
staff and m
the responsncies;
the commnding capab
er manage organizatio a tradition
their interd
Data Proces
tomatic datare normall considered
rt.
lecting and
d automatiform the Gl
automatic p
tion of the
re identificrameter valarameter tavior moden and acco
rs point of
view.
informative
anagement
time and t
nication issilities of eac
ent to havn structure
al approach,
isciplinary c
Fig. 2
sing
process syy dedicatedas an engin
processing tally fed by
obal Plant P
erformance
erformancetion,
idation,
ning,
ling,
nting of the
2/11
iew is influ
system ma
to have a u
he effective
es among th team;
direct infocapable faci could not b
mplexity.
Unbiased ap
stem gener to the di
eering activi
hese plant
the plant
erformance
analysis will
deficiencie
componen
nced by th
es it possibl
nique level
ess of the
he plant sta
rmation on tng and quice engaged d
roach
tes and storect procesty to be ma
rocess datautomation
Analysis .
cover the f
,
aging,
ir own spec
e to obtain
nd an unbi
rganization
ff and mini
he plant staly solving pue to difficu
es a lot of p control. Pually carrie
through ansystem, ma
llowing asp
ialization; it
etter result
ased vision
for the corr
ize the pro
tus;
erformance
lties encou
lant processlant perford out with li
informatiokes it possi
ects:
is not
s (Fig.
f the
ction
blems
issues
tered
data,
ance
mited
data
le to
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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:
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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
ge of
t the
from
arried
int of
essed
lation
. This
t and
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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
)
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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
Next
s as a
ns an
essed
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The
real
exac
The
real
what
Time
The
Perf
As rebut tdefinthe
suffevaria
rocessing oalue. In thisly happene
vents procealue. The e
happened.
Constant
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
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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
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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,
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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
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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.
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