Energy Conversion and Management 49 (2008) 3721–3728

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Energy Conversion and Management

journal homepage: www.elsevier .com/ locate /enconman

Component-wide and plant-wide monitoring by neural networksfor Borssele nuclear power plant

Emine Ayaz *

Istanbul Technical University (ITU), Electrical-Electronics Engineering Faculty, 34469 Maslak, Istanbul, Turkey

a r t i c l e i n f o a b s t r a c t

Article history:Received 7 March 2007Received in revised form 27 November 2007Accepted 29 June 2008Available online 15 August 2008

Keywords:Neural networksBorssele nuclear power plant (pressurizedwater reactor)Condition monitoring

0196-8904/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.enconman.2008.06.030

* Tel.: +90 212 285 6756; fax: +90 212 285 6700.E-mail address: ayaz@elk.itu.edu.tr

This paper presents a comparison of component-wide and plant-wide monitoring studies based uponneural networks for anomaly detection in Borssele nuclear power plant. The Borssele nuclear power plantis a unique nuclear facility to produce the electricity through the nuclear energy in the Netherlands. Thispower plant was established as a Siemens-KWU design and started to operate in the commercial sense in1973. However, it was modified under the backfitting programme in 1997. After the backfitting pro-gramme, its safety level was raised to the current state of the art. This paper covers the component-wideand plant-wide monitoring studies using the neural network approach for this modified case of thepower plant.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

In the operation of nuclear power plants (NPPs), one of the mostimportant applications in terms of the failure detection studies isneural network based monitoring. In this sense, there are so manystudies in the related literature [1–4]. However, the general aims ofthis kind of the studies focus on the identification of the failures inearly stage and diagnosis before significant accident from theviewpoint of the operational safety and maintenance costs.

Neural network applications play the major role in this field,specially, with their model-free structures and powerful nonlinearproperties. Hence, neural networks are considered as a powerfuland an effective technique in the nuclear power plant monitoring.Also, it has a compatible structure with the real time applications.To get the satisfactory results in real time and for wide-range sur-veillance studies, neural networks have been applied for reactordiagnostics in last decade [5–7].

Most of the previous studies on the monitoring and fault diag-nostics in NPPs with neural networks are based on the usage ofonly a single neural network. Whereas there is a limited numberof study that employ a combination of several neural networks.Fantoni and Mazzola [8] grouped the plant data first to the clustersusing self organizing maps and then used several specialized neu-ral networks each trained only in a limited and restricted plantoperational region. Embrechts and Benedek [9] stated that by com-bining several different networks the selectivity and robustness ofthe system could be ensured. Lee and Seong [10] developed an

ll rights reserved.

accident diagnosis advisory system based on an assembly of manyneural networks. Mo et al. [11] proposed a two level classifierarchitecture with a dynamic neural network aggregation model.All of these studies tries to detect major NPP transients and usethe generated data from a NPP simulator because the difficulty ofobtaining real data from NPPs.

Neural networks have been applied off-line and on-line since1990 to the Borssele NPP operational data and a special benchmarkon neural network applied successfully in SMORN-VII (Specialist’sMeeting On Reactor Noise) [12]. In earlier studies [13–15], neuralnetworks with various learning algorithms such as back-propaga-tion, radial-basis function networks, extended Kalman filteringwith adaptive learning in real time have been applied on the Bors-sele NPP. In recent applications Jordan and Elman type recurrent-type neural networks which has dynamic memory were used [16].

After the backfitting programme of the Borssele NPP of theNetherlands, it was reconsidered neural network based study forthis modified case of the power plant and extended to get compar-isons both of the component-wide and plant-wide monitoring ap-proach using the real NPP data. This research was developed underthis new viewpoint and formed.

2. Backfitting programme for Borssele nuclear power plant

The Siemens-KWU (Kraftwerk Union) designed 480 MWe Bors-sele pressurized water reactor (PWR) in the Netherlands went intocommercial operation in October 1973 and has had a good perfor-mance record, with only two trips from full power in the last tenyears. In 1993, after extensive analysis, the utility owner-operators

3722 E. Ayaz / Energy Conversion and Management 49 (2008) 3721–3728

decided it was worthwhile to embark on a $250 million backfittingprogramme, envisaging operation until at least 2007 [17].

In 1989 an inventory investigation was carried out by Belgatomand AIB (Association des Industriels de Belgique)-Vincotte to as-sess KCB (Kerncentrale Borssele) in the light of the latest regula-tions. This was followed by studies on special aspects such asearthquake and leak-before-break qualification. Also a detailedprobabilistic safety assessment (PSA) was carried out. Based onthe results of these investigations, a comprehensive safety conceptwas developed in 1991 by NV Elektriciteits-ProduktiemaatschappijZuid-Nederland (EPZ) in co-operation with Siemens-KWU. In thisconcept a new design basis for the plant founded on deterministicregulations combined with the findings of the PSA was defined.

Following the approval of the safety concept by the Dutch nu-clear authority, a lump sum/turn-key contract was awarded to Sie-mens-KWU in 1992 for the Project ‘‘Modificaties”. The total projectbudget was limited to US$250 million.

In 1993/94 an extensive licensing process, comparable with theprocedure for new plants, was carried out and in 1995 the hard-ware was ordered.

All today’s requirements as to earthquake, gas cloud explosion,aeroplane crash, single failure criterion, high energy line break,Anticipated Transient Without Scram (ATWS), 30 min criterion,internal fire and flooding etc. had to be fulfilled.

The external events requirements are met by the ‘‘protectedzone”. The already existing protected zone consisted of the reactorbuilding with its own diesel generator sets, its own instrumenta-tion, independent water storage and its own primary and second-ary feedwater systems. As part of the project the protected zonewas expanded to include an additional bunkerised building acco-modating the reactor protection system and a deep well systemfor long term cooling.

Subcriticality, heat sink and cool down is assured from this pro-tected zone totally independently from other buildings and facili-ties. A back-up control room was also installed in this area.

The single failure criterion is fulfilled by separation of the pre-vious feed lines and the headers of all cooling systems into inde-pendent sub-systems and by having redundant power supply.This called for more back-up electric power: three new physicallyseparated 4.5 MW emergency diesel generator sets were installed.The live steam and feedwater lines had to be replaced due to theleak-before-break criterion. The primary safety valves were re-placed by three Sebim tandem relief valves of compound construc-tion and directly mounted on the pressurizer ‘‘dome”, so all inletand outlet pipes are eliminated. The valves can cope with ATWSand are suitable for primary feed and bleed purposes.

To meet the 30 min criterion and to achieve better physical sep-aration it was decided to install a totally new reactor protectioncontrol system.

For accident management purposes a containment pressure re-lief system of the venture scrubber type, with fine metal filter, wasinstalled as well as a system with cathodic re-combiners to preventhydrogen explosion.

Table 1Economic comparison of the backfitting programme

Economic comparison (ct/kW h)

Cost component Gas fired combinedcycle

Coal firedstation

Borssele afterbackfitting

Fuel cost incl backend 5 4 2Investment cost 2 3 2.5Operation &

Maintenance cost1 2 3.5

Total 8 9 8

Given the large extent of the modifications it was impossible toadapt the existing control room to meet required man-machineinterface criteria. In the course of the project it was thereforedecided to build a completely new one. During the transition phasethe spent fuel pool and all necessary auxiliary systems were oper-ated and monitored using a smaller temporary control room. Inparallel a 100% replica simulator for the control room was orderedand prior to restart operators were trained on this simulator.

Also, comparison in term of the economic case and safety as-pects of the Borssele Backfitting Programme can be explained bythe following Table 1.

3. Reactor system in Borssele nuclear power plant

EPZ Borssele NPP is a two-loop pressurized water reactor(PWR), whose schematic is shown in Fig. 1. The primary circuitwater, which is driven by two primary circuit pumps, enters tothe reactor vessel from its one side and is directed down by thecore barrel. The water is circulated by the reactor, the heat exchan-ger and the pumps. Borssele NPP has two primary circuit pumps,thus two primary circuit loops. The heat energy conveyed by thewater is transferred to the secondary circuit water in the heat ex-changer. The temperature of the primary circuit water is about290 �C when it enters into the reactor vessel, and is about 320 �Cwhen exits from the reactor vessel. The pressure of the primary cir-cuit water is maintained about 150 bar by the pressurizer to pre-vent it boiling. Saturated steam is produced in the steamgenerator, which is placed at the upper side of the heat exchanger,and sent to high pressure turbines. Then the expanded steam in thehigh pressure turbine is came to low pressure turbines. Electricgenerator is driven by the turbines. Lastly the wet steam in re-duced quality comes to condenser and it is condensed, and then re-turns to the steam generator in contact with the primary circuit inthe form of compressed liquid [18].

A new data collection and diagnostics system is devised by theyear of 2001 and extensively used in the new operation in thestart-up of the new core 29 September 2001 and thereafter. Newmeasuring system of the Borssele NPP has been presented by Türk-can et al. in Annual Meeting of Nuclear Technology in 2001 [19].The 480 MWe Borssele PWR (KCB) is owned and operated byEPZ, and located near the Westerschelde estuary. The single unitplant was built over the years 1968 to 1973 by Siemens-KWUand achieved a life time load factor above the 80% over the first24 years. In the first half of 1997 the world’s most ambitious nucle-ar backfitting project successfully finished [17] and the powerplant operation in the core cycles in ’98 and ’99 achieved to aver-age load factor above 91%. The new plant data collection and pro-cessing system consists of two sub-systems [19]:

(a) Monitoring of plant DC signals of the plant (max 96 signals)with fixed sampling rate of 10 samples/s. used for continu-ous operational history recording with the aid of plant tran-sient analysis (MR-System).

(b) The reactor noise diagnostic system with measuring 32 AC/DC signals (MS measuring system) with aid of reactor noiseand primary coolant pumps induced vibrations.

Both systems build in National Instrument’s (NI) hardware andLabview software system and the continuous data of the both sys-tem is connected through the local area network (LAN) for the con-tinuous observation of the plant behaviour. In this paper, thesignals of the continuous operation of the plant through the DCmeasuring system (MR) with 96 process signals were used.

For the various application of the neural network, 62 channelsof the plant are used in consistent with learning procedure during

Fig. 1. Schematic representation and measurement signal positions of the NPP: (a) reactor system, (b) turbine-generator system.

E. Ayaz / Energy Conversion and Management 49 (2008) 3721–3728 3723

start-up. Table 2 indicates the used process signals which they alsoshown in the Fig. 1 of the plant representation.

4. Neural network topology and data selection for componentand plant based applications

Plant-wide and component-wide monitoring systems weredeveloped using an auto-associative neural network where the in-puts and the outputs are identically the same variables. The neuralnetwork was trained over the range of start-up operation using thesame data for the input vector and the desired output vector. Back-propagation using a sigmoid function was used to train thenetwork when the system was operating properly. Under theseconditions, the neural network outputs represent estimates ofthe instantaneous values of the output variables, and all of theseestimates are virtually identical to the actual outputs. When a sen-sor begins to drift or a failure is introduced into a data channel, theactual value (neural network input) changes, but the correspond-ing predicted value (neural network output) remains virtually un-changed because of the influence of all the other inputs throughthe connecting weights. Hence, monitoring the differences be-tween the estimates predicted by the neural network (outputs)and the actual values from the system (inputs) provides a methodof identifying drift or instrumentation system (or sensor) failure.

An alternative interpretation of these differences might be thatthe input–output relationship of the system from which the signalscome may have changed due to system failure or changes of somesort in the system [6].

In this study, for plant-wide monitoring 62 channels are usedtogether. The neural network topology and the channels are givenin Fig. 2 and Table 3. All networks in autoassociative mode that isinputs and outputs are same are trained with start-up data of 26 h,decimated to 10 s samples, and tested using steady-state data. Thedata used in this study was recorded on the dates between Sep-tember 30, 2003 and July 16, 2004. Training process is completeduntil acceptable learning accomplished. All networks have one hid-den layer and the number of hidden nodes is given in Table 3.

5. Neural network applications on the selected data

In component-wide monitoring the plant is divided into ninecomponents as core, generator, pressurizer, turbine, condenser,TA system which is the primary volume control system, RL Systemwhich is the steam generator water feeding system, Steam Gener-ator I and II. While deciding that which channels are belong towhich component it is considered some redundancy is allowedas some channels are used twice for two component. Each compo-nent is trained separately. The neural network topology and the

Table 2MR-Table (analog to digital converter (ADC) range = 0–5 V, gain = 2) signals used in neural network applications

Ch# Signal’s names Signal’s ranges Units

Lower Upper

2 Steam generator (SG) Water level (wide range) 0 12.055 m3 SG water level (wide range) 0 12.055 m4 Secondary steam pressure 0 100 bar5 Pressure of low pressure (LP) Turbine 0 60 bar6 Condenser pressure 0 100 bar7 Condenser pressure 0 100 bar8 Inlet valve position of high pressure (HP) Turbine 0 100 %9 Inlet valve position of HP turbine 0 100 %

10 Inlet valve position of HP turbine 0 100 %11 Inlet valve position of HP turbine 0 100 %13 Speed turbine and generator 45 58.33 s-114 Condensate pump pressure 0 40 bar15 Pressure of vacuum condenser 1 70 100 % vac.16 Pressure of vacuum condenser 2 70 100 % vac.17 Pressure of vacuum condenser 3 70 100 % vac.18 Level of condenser 1 0 1.829 m19 Level of condenser 2 0 1.829 m20 Level of condenser 3 0 1.829 m22 Primary pressure of loop-1 0 180 bar23 Primary pressure of loop-1 0 200 bar24 Primary pressure of loop-2 0 180 bar25 Primary pressure of loop-2 0 200 bar26 Primary coolant temperature (hot + cold)/2 loop 1 275 325 �C27 Feed water flow pressure (steam generator 1) 0 160 bar28 Primary coolant temperature (hot + cold)/2 loop 2 275 325 �C29 Primary coolant temperature (hot + cold)/2 both 275 325 �C30 Primary coolant cold leg temperature (L-1) 0 400 �C31 Primary coolant hot leg temperature (L-1) 0 400 �C32 Primary coolant cold leg temperature (L-2) 0 400 �C33 Primary coolant hot leg temperature (L-2) 0 400 �C34 Pressurizer level 0 8.22 m36 Pressurizer temperature (water) 0 400 �C37 Pressurizer pressure (water) 0 200 bar38 Neutron detector (50) 0 120 %39 Neutron detector (60) 0 120 %40 Neutron detector (70) 0 120 %41 Neutron detector (80) 0 120 %42 Electric power �125 500 MW43 Reactive electric power �350 400 MVar46 Secondary steam pressure (Loop-1) 0 100 bar47 Secondary steam pressure (Loop-2) 0 100 bar48 Secondary steam mass flow (Loop-1) 0 450 kg/s49 Secondary steam mass flow (Loop-2) 0 450 kg/s50 Secondary steam temperature of SG (L-1) 0 350 �C51 Secondary steam temperature of SG (L-2) 0 350 �C52 Primary volume system control tank pressure 0 25 bar53 Primary volume system control flow (to loops) 0 14 kg/s54 Primary volume system control flow (from loops) 0 11.2 kg/s55 Primary volume system control tank (Level) 0 1.5 m56 Control rod position L-bank (1 step = 1 cm) 0 100 st(appen)57 Control rod position D-bank (1 step = 1 cm) 0 300 st(appen)61 Emergency feed water pump 21 pressure 0 160 bar62 Emergency feed water pump 22 pressure 0 160 bar63 Emergency feed water pump 23 pressure 0 160 bar64 SG-1 water level (narrow range) 4.385 12.055 m65 SG-1 water level (narrow range) 4.385 12.055 m66 SG-2 water level (narrow range) 4.385 12.055 m67 SG-2 water level (narrow range) 4.385 12.055 m68 Feed water flow rate (SG-1) 0 450 kg/s69 Feed water flow rate (SG-2) 0 450 kg/s70 Feed water flow press (SG-1) 0 160 bar71 Steam generator level (SG-1) 0 12.055 m

3724 E. Ayaz / Energy Conversion and Management 49 (2008) 3721–3728

channels are given in Fig. 2 and Table 3. Fourteen channels are usedin some networks twice. These channel numbers are Ch# 6, 7, 13,22, 23, 24, 25, 26, 28, 31, 32, 33, 68, and 69.

The comparison of component-wide and plant-wide neural net-work’s result can be gotten from two different viewpoints. Fromthe viewpoint of same channels used in both networks, for Ch#13, 22, 23, 24, 25, 26, 28, 31, 32, 33, 68, and 69, it is not observedmuch differences. But, for Ch# 6 and 7, it is observed small differ-ence. Fig. 3 shows the result for Ch# 6.

In Fig. 4, one of neutron detector signals is shown. As seen fromFig. 4, it is clear that the learning stage is sufficiently good for bothnetworks. Here, the testing stage result of the component neuralnetwork for core is better than the result of neural network forwhole plant. In terms of the remaining three neutron detectors,the similar results are observed. Fig. 5 shows the similar observa-tion for the control flow signal.

On the other hand, the drawback of using component-wide neu-ral network is to have a few signals for some components. And, if

Fig. 2. Auto-associative neural network topology used for each component in theapplication.

E. Ayaz / Energy Conversion and Management 49 (2008) 3721–3728 3725

one of the signals has high peaks, then a satisfactory testing resultmay not be obtained. A figure related to this argument is shown

Table 3The inputs and outputs used in component-wide neural network monitoring

Components Plant channel numbers

Core Ch# 22, 23, 24, 25, 26, ,28, 29, 30, 31, 32, 33, 38, 39, 40, 41Generator Ch# 13, 42, 43Pressurizer Ch# 34, 36, 37Turbine Ch# 4, 5, 6, 7, 8, 9, 10, 11, 13Condenser Ch# 6, 7, 14, 15, 16, 17, 18, 19, 20TA system Ch# 52, 53, 54, 55RL system Ch# 61, 62, 63, 68, 69Steam generator I Ch# 2, 22, 23, 26, 31, 46, 48, 50, 64, 66, 68, 70, 71Steam generator II Ch# 3, 24, 25, 27, 28, 32, 33, 47, 49, 51, 65, 67, 69Overall plant All channels given in Table 2

0 0.5 1 1.5 2 2.5 3

x 104

22

24

26

28

30

32

34

36

Pattern

Pattern

Ch#

6 :

Con

dens

er P

ress

ure

(bar

)

Ch#

6 :

Con

dens

er P

ress

ure

(bar

)

0 0.5 1 1.5 2 2.5 3

x 104

0

2

4

6

8

10

12

14

16

Dev

iatio

n (b

ar)

Nnet for CONDENSER

deviation

measurementnnet output

0 0.5 1 1.522

24

26

28

30

32

34

36

0 0.5 1 1.5

Nnet for PLA

Fig. 3. Neural network’s test result for th

by Fig. 6. Here, since the generator’s reactive power signal has somepeaks, these affect the neural network’s response. Because of this,for the neural network application related to the generator, the ac-tive power signal has some peaks due to peaks in reactive powersignal. Therefore the testing result of the neural network for wholeplant is better than component-wide one, since the peaks for plant-wide application are compensated by the neural network.

In the Table 4, represented situations are the most noticeableparts in showing the results of the related comparisons both ofthe component and plant-wide applications. According to these re-sults of Ch #6, 38 and 54, except Ch #42, each neural network’sperformance during the test stage of the networks as seen in Figs.3–6 for the component-wide monitoring is better than the plant-wide case although their performances are very close to each otherin the learning stages and this situation is shown by the total rel-ative error level as seen in Table 4.

Number of inputs and outputs Number of hidden nodes

, 56, 57 17 153 53 59 109 104 55 5

13 1513 1062 50

0 0.5 1 1.5 2 2.5 3

x 104

22

24

26

28

30

32

34

36

Pattern

Ch#

6 :

Con

dens

er P

ress

ure

(bar

)

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x 104

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16

Dev

iatio

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ar)

Nnet for TURBINE

deviation

measurementnnet output

2 2.5 3

x 104

measurementnnet output

2 2.5 3

x 104

0

2

4

6

8

10

12

14

16

Dev

iatio

n (b

ar)

NT

deviation

e Channel #6: Condenser pressure.

0 5 1 1.5 2 2.5 3

x 104

0

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30

40

50

60

70

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90

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Pattern

Ch#

38

: Neu

tron

Det

ecto

r (%

)

measurementnnet output

0.

x 104

0

20

40

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Dev

iatio

n (%

)

Nnet for CORE

deviation

0 0.5 1 1.5 2 2.5 3

x 104

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Ch#

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: Neu

tron

Det

ecto

r (%

)

measurementnnet output

x 104

0

20

40

60

80

100

120

Dev

iatio

n (%

)

Nnet for PLANT

deviation

Fig. 4. Neural network’s test result for the Channel #38: Neutron detector.

0 0.5 1 1.5 2 2.5 3

x 104

3

4

5

6

7

8

9

Pattern

Ch#

54

: Con

trol F

low

(kg/

s)

measurementnnet output

0 0.5 1 1.5 2 2.5 3

x 104

-2

-1

0

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8D

evia

tion

(kg/

s)Nnet for TA SYSTEM

deviation

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Ch#

54

: Con

trol F

low

(kg/

s)

measurementnnet output

0 0.5 1 1.5 2 2.5 3

x 104

-2

-1

0

1

2

3

4

5

6

7

8

Dev

iatio

n (k

g/s)

Nnet for PLANT

deviation

Fig. 5. Neural network’s test result for the Channel #54: Control flow.

0 0.5 1 1.5 2 2.5 3

x 104

-100

0

100

200

300

400

500

Pattern

Ch#

42

: Ele

ctric

Pow

er (M

W)

measurementnnet output

x 104

0

100

200

300

400

500

600

Dev

iatio

n (M

W)

Nnet for GENERATOR

deviation

0 0.5 1 1.5 2 2.5 3

x 104

-100

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Pattern

Ch#

42

: Ele

ctric

Pow

er (M

W)

measurementnnet output

x 104

0

100

200

300

400

500

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Dev

iatio

n (M

W)

Nnet for PLANT

Fig. 6. Neural network’s test result for the Channel #42: Electric power.

Table 4Comparisons for neural network results

Comparisons for channel numbers (Ch#) Component-wide: relative error level Plant-wide: relative error level

Core Ch# 38 540e�3 (Better) 192e�2TA System Ch# 54 752e�3 (Better) 328e�2Generator Ch# 42 917e�3 500e�3 (Better)Ch#6 863e�3 Turbine, 357e�3 Condenser (Better) 771e�3

3726 E. Ayaz / Energy Conversion and Management 49 (2008) 3721–3728

Table 5Relative errors given for component-wide and plant-wide neural network models

Ch# Core Generator Pressurizer Turbine Condenser TA System RL System Steam Generator I Steam Generator II Overall plant

2 422e�4 501e�43 896e�4 200e�34 216e�3 241e�35 837e�3 408e�36 863e�3 357e�3 771e�37 806e�3 577e�3 700e�38 333e�2 378e�29 155e�2 157e�2

10 321e�2 285e�211 103e�2 149e�213 287e�5 935e�5 382e�514 724e�3 615e�315 135e�3 155e�316 724e�4 116e�317 113e�3 122e�318 985e�3 564e�319 388e�3 402e�320 530e�3 590e�322 396e�4 416e�4 338e�423 328e�4 378e�4 383e�424 407e�4 229e�4 484e�425 392e�4 360e�4 404e�426 174e�4 971e�5 141e�427 295e�3 277e�328 106e�4 107e�4 159e�429 104e�4 174e�430 355e�4 475e�431 169e�4 178e�4 197e�432 396e�4 296e�4 394e�433 186e�4 188e�4 296e�434 633e�4 877e�436 120e�4 162e�437 263e�4 614e�438 540e�3 192e�239 606e�3 193e�240 748e�3 212e�241 440e�3 173e�242 917e�3 500e�343 213e�2 100e�246 300e�3 293e�347 354e�3 290e�348 977e�3 547e�349 885e�3 591e�350 143e�3 365e�351 172e�3 285e�352 651e�2 588e�253 175e�2 227e�254 752e�3 328e�255 602e�3 704e�356 211e�2 203e�257 328e�2 321e�261 800e�3 232e�262 126e�2 136e�263 626e�3 117e�264 712e�5 198e�465 111e�4 334e�466 598e�5 242e�467 418e�4 497e�468 587e�3 970e�3 708e�369 621e�3 914e�3 708e�370 276e�3 281e�371 555e�4 560e�4

E. Ayaz / Energy Conversion and Management 49 (2008) 3721–3728 3727

6. Conclusions and discussions

Monitoring and detection of faults before they cause to seriousproblems has a vital importance in terms of safe and economicoperation of critical systems such as nuclear power plants. Neuralnetworks as one of the signal based method have been applied tomonitor the system’s behavior. In this study, Borssele NPP is con-sidered and monitored by neural network approach. For this aimfeed-forward back-propagation neural networks are used to deter-

mine which combination of the inputs affects the output of theneural network. In this sense this study gives a comparison of be-tween component-wide and plant-wide monitoring of the BorsseleNPP using the real operation data. For this purpose, the system isdivided into several sub-systems. And for each system a neuralnetwork is established. Finally, the performances of sub-system’sneural networks are compared with that of overall system’s neuralnetwork. The most noticable differences has been shown in Figs. 3–6 and given in Table 4 for condenser pressure (Ch #6), neutron

3728 E. Ayaz / Energy Conversion and Management 49 (2008) 3721–3728

detector (Ch #38), control flow (Ch #54) and electric power (Ch#42) signals. To compare the results of both component-wideand plant-wide monitoring for all of the plant signals used in neu-ral networks, the relative error levels is given in Table 5.

As seen in Table 5, it can be observed the superiority of theoverall plant modelling approach over the component-wide basedapproach for some channels. In this sense, specially, for channelnumbers 5, 6, 7, 10, 13, 14, 18, 22, 24, 26, 27, 32, 42, 43, 46, 47,48, 49, 52, 56, 57, 68 and 69, the performance of the plant-widemodelling is better than the component-wide approach. It meansthat the 23 channels of the total 62 channels which are the numberof the channel numbers used in this study. Hence the success of theplant-wide modelling is approximately 1/3 according to the totalchannel numbers. From this viewpoint the success of the compo-nent-wide modeling is approximately 2/3 of the overall channelsand it shows the advantage of the component-wide approach overthe plant-wide approach.

Hence this study is presented as an alternative tool to demon-strate the performance of the neural networks with the wholeand sub-system approaches in the complex systems like the nucle-ar power plants and the success of the component-wide neuralnetwork application for Borssele nuclear power plant in the Neth-erlands is denoted.

Acknowledgements

The author gratefully acknowledge Erdinç Türkcan and NV-EPZBorssele NPP Authorities for their interest and support.

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