8.1.2011 1
Methods of Czech VVER
Process Parameters Validation
Jindřich Machek, NRI Rež plc
VVER – 2010
EXPERIENCE AND PERSPECTIVES
01 – 03 November 2010, Prague, Czech Republic
AUTOCLUB CR, Opletalova street 29, Prague 1
8.1.2011 2
Methods used for validation
Simple physical models (e.g. P-T diagram,
conservation equations)
Special regression model (core exit
temperatures)
Neural network (PEANO)
Transparent network
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P-T diagram used for validation
(condenser of Temelin unit 2)
-97.0
-96.0
-95.0
-94.0
-93.0
-92.0
-91.0
29 30 31 32 33 34 35 36 37
2RM21,2,3T001
2S
D01,2
,3P
001 [
kP
a]
PT1
PT2
PT3
P-T teor
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P-T diagram with invalid measurement (unit 1)
-96.0
-95.5
-95.0
-94.5
-94.0
-93.5
-93.0
29 30 31 32 33 34 35 36 37 38
1RM21,2,3T001
1S
D01,2
,3P
001 [
kP
a]
PT1
PT2
PT3
8.1.2011 5
Reconstruction of invalid signal pressure calculated from temperature
-97
-97
-96
-96
-95
-95
-94
-94
07/03 06:00 07/03 18:00 08/03 06:00 08/03 18:00
time
1S
D0
1,2
,3P
00
1
1SD01P001_N1
1SD02P001_N2
1SD03P001_N1
1SD02P_calculated
1SD01P_calculated
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Core exit temperatures
Core exit temperatures validation using measurements in the
vicinity
– Specialized method for highly correlated homogenous measurements
only
– Uses 2 – 15 other thermocouple measurements in the vicinity
– Simple and relatively precise model
To = Σ ki Ti
under the condition Σ ki = 1 (isothermal condition).
– Good experience with extrapolation to unknown states (valid during
transients, only slightly increased deviations)
8.1.2011 8
Core exit temperature validation – example 2
Validation of the T147 measurement
260
265
270
275
280
285
290
295
300
305
14/12/2003 14:52 14/12/2003 15:07 14/12/2003 15:21 14/12/2003 15:36 14/12/2003 15:50 14/12/2003 16:04 14/12/2003 16:19 14/12/2003 16:33
Time
Te
mp
era
ture
[°C
]
T147
Rec_1
Rec_2
To
T144
T164
T165
8.1.2011 9
Implementation of PEANO at Temelin
Main goal – establish the model for the power production and
validate parameters used for the model
Parameter selection – 35 signals from Temelin Unit 1 and 2
(electric power + II. circuit characteristics),
data from Jan to Apr 2009
Pre-processing – data check using own conversion application
(*.csv →*.dat) . Elimination of gaps (missing data), deleting
records with too many gaps. No filtering, no denoising.
AANN training – all data used simultaneously for training and
validation
Validation using PEANO Batch Monitoring Module
8.1.2011 10
PEANO results
Frequent overcrossing of ±3σ and ±0.5% of the extent associated with fast transient and resulting in „check“ or „failed“ status of the signal:
Subsequent analyses show probably no serious problem with
measurement quality except following case.
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PEANO results
One systematically invalid measurement found – pressure in the
middle part of the condenser on unit 1
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Electric power validation
Transparent network– Specialised method for complex parameters like unit electric
power
– Easy reasoning
– Solves the problem of stand-by systems
– Precision competitive with AANN (PEANO)
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Conclusions
Validation is useful – capable to identify potentially dangerous errors in
the I&C system
PEANO – user friendly tool, enabling to validate the systems without
knowledge of physical relations between variables
PEANO estimates: high precision with known data, problems with
unknown data evaluation, namely during transients
PEANO disadvantages:
– Low capacity
– Frequent request to check validity – some guidance how to do it or coupled
diagnostic system would be welcome
Core exit temperatures validation – in some cases simple correlation is
powerful enough to give equivalent results
Transparent network:
– Easy reasoning
– Accuracy lower than PEANO on known data but reliable even in case of
unknown transients
– Overcomes the problem with stand-by parts
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