An American Nuclear Society International Topic f Meeting 4%wf.- f5W y-4,

SAFETY OF

OPERATING REACTORS

PROCEEDINGS

September 17-20, 1095 Hyatt Regency Seattle (Bellevue), Washington

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or use- fulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any spe- cific commercial product, process, or service by trade name, trademark, manufac- turer, or otherwise does not necessarily constitute or imply its endorsement, recom- mendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

ABmcr A combination 8pprOach of an expert system lad

ptml netwarits is used to implement a prototypt lfvue accident diagnostic system whicb would monitor the Prognssian of tbe sevtfc accident and provide plant atatus information to assist tbt plant staff in accident olanagtmenl during the accident. Tbe station blackout

study case. The m e n 1 phase of research focus is on distinguishing d i€fmt primary system failure modes and following the accident oaosien! before and up to vessel &each.

occi&nt iDa prrsornszed water reactor (PwR)isuredas tbe

1. WlRoDucTION

283

fcsults &ow that tbe neural w t w b can successfully ntrleve the pattuns even with large random noise and panial loss of tbe input infotmtjon. As indicated by the urthors, this kind of amr ntirtancc might be useW in Seven %&dent rituations wben the iosoumaltation may mot be avaiIabk because of tbe bsnb cnvironmcni. Neural networks have .too bee0 used in many other areas of nuclear power planu, including umsient diagnostics, sensor validation, plant-wide monitoring, deck valve monitoring, urd vibration analysis7 in most of these applications, multi-layer, feed-forward backpropagation neural networks uc used. A dynamic node arcbjttcture rcbeme for nauaI netwo& training was proposed by Basu and Bartleu to Optimite tbe naxd network stnrcnne8 For a rhta &ye? backPopagation ~eural network, while tbe ncmn Dumber of input lad output layers is usually determined by the diagnostic problem, tbc number of oupmsfathebi&u byerisdded adekted dynamically duriDgthc rraioiag until tbeopoinral criteria are met witb a artaia numba of hidden ~~urons. Neural networks with rbemes 0th rban -&on bavc also been applied 10 fault diagnosis. Specbt's probabilistic neural networks WCIT modified and integatcd witb influence diagrams for power plant monitoring and &gnostia!~10 Marsegucna and Zio OSqKMed 8 anrh?tctic ~eu t r r l network (boltnnann machine) urd used it to diagaosc a pipe break in a simulwd wxiliay ttadwaocr ,plan."

It is impcmmt fix !he pmonnel in charge of accident ~~t during &e Widcat to lmdcntand the status of tbepow#jhnt d tbe pmpessioa trtnd of tbc accident in order to evsluate and implement effective pnvention or mitigation rurutgks. WUle there arc lots of effons on diagnostic systems for rccidtnts before core damage,12*13,14 &en i s I general lack of diagnosis metbodologies for t t y ~ t accidents where the core would r m d a g O S W a t d a m a g e I n b P e d d t n t s ~ g h t ~ k y ~ vessc)irach.

A combination approacb of an cxpcrt system and neural wtworks is usd to implement a gmotypc seven accident diagnostic system wbich would monitor the progression of the scm 8ccident and provide necessary

management during the rccidenr The station blackout

study case. The cunent phase of ~uearcb focus is on

following the accident msient befane md up to v& brcacb. Section I1 is a brief discussion of the station blackout accident. Section III pnsr:nts the diagnosis mctbodology. Section N describes neural aetworks and Ibe expert system. Section V sbows tbe ~ w a l network aainsng nsults. And finally, Section VI[ is the summary of the work.

plant uatps information toassist the pkmt s t a i n accidalt

4ccident fn 8 - w b tbc

diS@g&hbg di f fwt primarlV ryStemi tail= modes PPd

II. STAnON BLACKOUT ACCIDENT

One of the major type of accidents .is Starion Blackout, which conoibutes a relatively large risk to nuclear power p h t operation and might progress to the stage of seven core damage or ftrrtber. Station BlachDut iS the situation when both offsitc and onsitc AC power arc unavailable. During these accidents, thm are varic~w primary system failure modes, including nactor coolaait pump (Re) real failure, power operated relief valve (PCIRV) stuck open or safety d e f valve (SRV) stuck open, tcmgerahur-induced

steam generator tube rupture QSGTR), and vessel breach 0%). At the start of the accident, then is loss of RCP seal cooling because of a loss of AC power. Large 06 small seal failures might develop and cause &e h a of rtactof coohnt system (RCS) inventory. Then atso might be primary system inventory Ioss through the PORVs or SRVs cycling. With the uacovery of the am, the PORVs or SRVs would opeiate at amuch higher ocrnpanrn than the normal condition and might fail to m:lose during one of tbe cycles. ISGTR M creep rupture of the bot lcglsurge line might also bappcn if the system is exp- LO supuheated steam an3 hydrogen due to nahrrat CircUWon over a pcriod of time under high differential pnsstue. If any of these occurs, tbe RCS might be dtpressurized and the vessel might fail at intermediate or low premrr; and baKx ahigh pressure melt injection, wbicb may cause containmeat failure by direct beating, would be pnlikely to happea If none of these happens, the VB will pmbably occur at high pressure and direct containment bcaiJng migbt happen. After vessel breach, the accident will rrrntinuc to p p s and the containment might be endangcml and fail, if it is n0t;rlreadyfailOdatvB. *

bot leg/Srrrgt Line faiIW (HIS Faif=), tcmpaature-induced

ReIiminary analysis indicates that primary system pressure undergoes m a OT Iw distinctdynamic respanscs

284

witb vtvious faiIurc BkodcL duriog station blackout.3~15 After the initial QppsIent period tbuc is a-e of the pduuuy rystcm pnssrtn bcausc of energy transfer to the ~ s y s ~ b c f o n t b e d r y ~ o f r t e a m g e n t r a t o r s a p b possible a r a g y loss dwough rbc prknsrv system opening (e.& RCP sed leaking). Afier the dryout of h e ilcam ~ e b t n u o l t , t b t ~ ~ ~ ~ ~ t O t b c PORV setpoiat wben the PORVS start cycling. The pimpiv s y s m p m x ~ will fluctunte acmrdbgly. For tbc case of large RCP seal failure, the pessun drop might k so kge that the pressure will DO longer go up to tbe PORV setpoiat D~pendhg 00 different p!imary failure modes, tben might be a diffennt primmy s y s m pressure history. hi rddih, tben W? otbn K W -8s wbicb could be used to disthguisb @fmt failure rnodcs.16 For example, when ISGIR occ~us, the pressure, temperanut, and radiation level of the secondary side of b e stcam generator will normally increase. In summary, the combination of the pxhnry system p s s m bistary and other insaumeatation indications could be used to diagnose Various primary system failure modes during station blackoutmts.

l? I .MEmOrnrnY

Tbtn an basically two fundamental problems for the diagnostic task, i.e, detection of a failure and identification of the failure. The detection proass would uncover a possible primary system faifwe from abnormal sensor readings and the ideotification process would determine which failure actually occ~ps &om the time series of the signals. It is important to distinguish these two steps of

what exactly happens afta the detection. Fa aample, il is rather easy and quick IO !ell drat tbe rtactor vessel has been breached, of the bot Ieg/surge b e fails, from the sudden large decrtase of the primary system prrssm, w b c m it is hard to see right away whicb of tbest two happens. For tbc case of PORV Stuck Opcn, tbc failwe could not be dtrtatd for some SuStsiaCd period of time ma the sensor rradings show 8ubstantial abnormality. Tbe same situation applies to ISGlR witbout radiation rrading of the secondary side of S f t a m generaton. Various ancertainties have to be CQllsidCrcd during tbe -dent progrtssion. First, thsc is

during a station blackout rcddcnt, tbe puxiljary ftedwaur system may eitber be io operation of fail at the initiation

there dght k failure of sttam generator tubes, failure of the bot kglsurge line, Q a stuck ope0 power opmtcd rebeef valve. Second, there i s uncenainty regarding wbtD rbe f&rtn accUn. 'IEe thing of each possible failure is bard to dctljmine. It b DOt possible to sptcify exactly wben Lk power opmtcd relief valve would be stuck open tmda

tbe & m b it &y t&& mon daU to ideotify

tmCClUhty Rgarding Which failure =EM. Fa CxamplC.

of tbe rrddenr Mtn uncovcry of the top of the active fuel

Tbe expert system will be used to monitor tbe pgrcssion &am the start of the atxidenu. ?be initial accident conditions and majar change of plant sgtus will k

when the diagmstic neural networks should k initiated for f&lrp.e detection md identification. The diagnostic nsulu from neural networks will be compared, if possible, with tbc muhs 6om the expen system. The difference between &e actual msor reading during the accidents nnd Ibt MAAP simulation will be & o m in order to justify tbe ost of ocmal networks and accommodate large uncednty. Wsimulation codes could garme the primarV systmr prcssm history and other indications, e.&, secondary side pressure and temperature, containment temperature and ~ ~ C S S W C , radiation levels. "be results will also provide bounding vslucs and timing iafonnation of tbe failures. Thus, MAAP nm results am used to gain qualitative, semi- qualitative, and quantitative instrument reading change

Other scientific howledge and caginu*ring judgment wiU

recmded and displayed. This system will ab0 derermioe

ptuerns to fOrm tbe kaOWkdge base Of the C X p SySttm.

P J s o k ~ t c d ~ t o t b e i s o w l e d g e b a s e .

Tbe transient data ftom W n m s can k used to min the aetworks to distinguish various failure patlenrs. since tbe timing of the failure i s uncertain, &e rrsdts of nsc of ncmal netwurks for diagaosb purpose must be tnated cantioudy since tbe wraal DftwoEk aaining highty depcods ob the )cwDBljos, CVCD tbougb tbe neural oetwarks swain some capability of resistance to rim noise. 'Ibe mining of tbe neural ~ttworks oeetfs to be studied in view of LCVW ~~CtCtBinties. including variability in initial conditions, diffa-encts between MAAP and actual performance, changing configuration after

and otbu amsidaations will be examined Lo arda to we initiation of MAAP, misleading sensor signals, et$. n e =

265

fv.NEUWUNETWORKS ANDMPERTSYSTEM

'Ihe buman baio oeoamglitbcs wry complicated tasks by using billions of rhple Deurons which axe taterconaemd. M i neural nctwQPL;s an the computer simulations of buman brain f~nction.~~*~**19 Tbese networks have many anificial Dtlpops, usually called processing elements. Tbes procrasing elements are arg- in iaycrs and bave limillt functions 85 buman neurons by ndding op tbe weighted values of the many tnpuu. Tbt input layer acts as a buffa for tbe input data. The output layer acts as a buffer for tbe output results. Then might be one ZE mon hidda laym in between. A learning process is sccomplished by pnxating bo& input data and desired output results tnd t b c ~ obtaining tbe weigbting coeffiidaru rmmg fayem of procwing elemenu by some Iearniag atgaritbms. During tbe d l process. tbe baiacd wuzal network talres inputs and genuaus outpul nwlts. Figure 1 Wws tbc baric smctauc of the diagnostic neural networks. 1 i s I tbree-layer, feed-fomard. backpropagation rrcrtal network. Tbc MAAP dm is used to traia tbc neural oetworks wbicb UT tben rested against all the other rcenarios. To some extent, this would gparanw the geanality of bw OCIIfaf networks to &tea and idtntify the faults wda vllsious cooditions.

The expert system will provide tbc general environment for monitoring tbe overall plant status. determination of rreural networks usage, displaying DtQessary infarmation. Tbe expen rysum also provides bdepcndent ryuun frilw diagnosis, if possible. Tbt software used for &e popostd cxpcn system will be W P E R T OBJECT.20 whicb Is I commercial software mdtr rbt lBM K! window environment. IF-- rules an used for badrward masoning and forward reasoning. Figure 2 rbows tbe logic flow of tbc diagnostic system.

ourput layer

biddeo layer

input layer

PPS Priuwy System Rerture PSG: Stcun Gcnmtor Pressure TSG: Stwr &tot Temprrturc PB: CuataixmcntPressure TGA: Coctrirrmtat Tcmpnaoe

FIGURE 1 A 'RiREE LAYER BACK PROPAGATION NEURAL W O R K

I W

FIGURE 2 =IC FLOW OFTHE PRO'IWYPE SEVERE ACCIDENT DIAGNOSTIC S Y S W

To evaluate tbe data adquacy for diagnosis and to detrnntne tbe data for neural network oaining for failme dcttcrian and identification, training data was taken 6an tbt sm of tbe failure and tbe amount of data was progressive inaeased ( e v q 20 second step). Tbt input neurons are bermmined BccofdiDg to the amount of data for mining. There are two output neurons witb mapping rcbeme far mining &own in table 1.

TABLE 1 NETWORK TRAINING FOR DATA EVALUATION

MAPPING SCHEME FOR NEURAL

Fourteen groups of data (3x20s, 4x20s, 5 x 2 k 6x20s, 7 x 2 0 ~ ~ W O s , 9x20~~ 10x20~~ 13x20s, 14x20~~ 15x20s, 16X2oS, 17~20s~ and 20x20s) from AS1 (Vessel BnaCn), AS21 QSGTR), ASZb(lSGTR), AS3a(WS Failure), ASQa(PORV Failure) w m used for the mining. Seasor dataiswmsalrzed ' between 0.0 and 1.0. For the network rccaU ploccss, my data kss than 025 is tnatuI 8s 0. my btlabwe 0.75 is OW as LO* my data betwetn 035 and 05 is 8wted as likely 0, any data betwe.cn 05 md 0.75 is Dtwd I Rely 1. "be mapping rcbane usad far testing is rbowp in table 2.

Table 3 to Table 6 show the tea rtsulls. Case 1 b the mS initially workinn and no RCP Seal Failure case.

2 is Ulc-AFWS kitially working witb RCP Seal

287

I

Failure case. Case 3 i s the AFWS initially Non-working and no RCP Seal Foilwe case. Crse 4 is tbe A F W S ioioial)~ Nm-w-g with RCP Seal F o i k ca~e.

TABI€2 OUTPUT MAPPING SCHEME FOR TESTING FDR DATAEVALWAllON

I output 1 Output I MapPbS Neurin2 I Nertronl I .-

0.0 - 025 I 0.0- 025 I Vessel BItacb 0.25 -05 0.0 - 025 0.25 - 05 0.0 - 025 0.75 - 1.0 0.5 - 0.75

0.0 - 025 025 - 0.5 0.25 - 0.5 0.75 - 1.0 0.75 - 1.0 0.5 - 0.75 0.5 - 0.75 0.0 - 0.25 025 - os 0.0 - 025

liktly Vesscl Bnacb l iwy Vessel Btcacb lJCtly Vtssl Brcacb

ISGTR likely I s m iikely ISGTR likely ISGTR

WS Failure iikely WS Failure likely WS Failure

0.5 - 0.75 025 - 0.5 I likel; WS Failure 0.75 - 1.0 0.75 - 1.0 I PORV Failure 0.5 - 0.75 0.75 - 1.0 iiktly PORV Failute 0.75 - 1.0 0.5 - 0.75 UCIY PORV Failure

i 0.5 - 0.75 0.5 - 0.75 likely PORV Failure With tbe ipaeesc of the data into the failure, tbe neural

networks rmR ability converges to a certain level where &SI nsults lllt no longer improved witb more data From thensulu, the converged time data far VB and WS Failwe i s 3x20s. Tbt converged time data for ISG'lR and PORV Stuck Open i s 15x20s.

Finally, tbe Detection Neural Networks and I d c p t i f r c a t i o n N e ~ N e t w o a k w a ~ ~ ~ ~ . F o t t a c b o f tbe two neural oetwotks, &ere arc 80 input neurons rejxcscnting 15 lime steps of data of 20 second cacb. ?bere ate thne oytput wmns witb tbc mapping scheme show la table 7. 'ibe aaioing camples included data of tbe no fpilun case. ?be trainiag data fa Vtsscl Bnacb and HIS Faifrpc ranged from 3x20s to 8x20s into tbe failw. T b e Oaining data for ISGTR md PORV Stuck Open ranged from 10x20s to 15x20s. Since the number of input neuroos i s fixed at 80 01 13 time steps of 20 seconds, most training data also coven a porrion of tbt no failure w e . Figure 3 and figure 4 show tbe amvngcnce of the mining of &e P e d net- which combines tbc expcn system 8 D d b K ~ D u W Q ) I s .

http://betwe.cn

TABLE3 TESTRESULTS FOR VESSEL BREACH I D ~ C A l ' 7 O N

TABLE 4 TEST RESULTS FDR H O T m U R G E LINE IDENTIFICATION

TABLE 5 TEST RESULTS FOR ISGTR I D m C A T I O N

TABLE 6 E S T RESULTS FOR PORV STUCK OPEN IDENllRCATION

TABLE 7 MAPPING SC1iEME PORDFzEcrON AND IDENLIRCA'IION NEuRAt NETWORKS TRAINING

CASE NAME Output Neuron 1 Output Neuron 2 Output Neuron 3 No Failure 0. I 0.1 0.1 Vessel Breacb 0.9 0.1 0.1

0.9 I 0.1 1 0.9 WS Faillure 0.9 0.9 0.1 PORV Failure 0.9 I 0.9 I 0.9

288 _ _

RGURE 4 ?RAINING CONVERGENCEFOR XDENTlFlCATION NEURAL NETWOW

Wha, networks were tested, VB or WS Failure could bc detected 20 seconds into the failure and identified 30 seconds Zoto tbe failwe. Tbe diagnosis could be amfhned fm seeveral more time steps. Tbe ISGTR or PORV Stuck @en could be detected 160 seconds into the failure and be idcptifred 180 seconds into the failure. Test of DO failure cases was successful. When 30% of random noise was added 10 tbe training data, the Dewtion Neural Network auld stiIl comaly dettct various fail-. Vessel Bnacb of 3% Fpilure could be correctly identified by the fdcntification N e d Network with 25% random noise added to the training data. PURV Stuck Open and ISGTR couldbecanctlyidcacifredwitb IO%rapQaanabse.

?be Detection Neural Network and Identification N e d Network can be initiated draing tbe cycling of tbe WRV, long before tbc stan of primary systun failure. Evcry ZOrecaads, new time supdata can be fed inand the

oldest time step data thrown out. If tbe situation is classified 8s No Failure by tbe Detection Neural Network, tbe process would continue. If some faiJrw is detected by the Detection Neural Network, the Identification Neural Network would be initiated to identify wbicb failure occmnd. Funbet data input would be used for diagnosis confumadoa. 'ibe expen s y s m would provide repatale confirmarion, and would advise of differences between (ensar readin@ rad the MAAP Itsults.

VI. SVMMARY

Artifidat tatClIiigcace, rucb as cxpest systems and neural networks, bas been used to detect urd identify ptimary system failures during station blackout. Among tbe Wings Iccompfisbed, tk use of neural networks to

of sucb a technique. 'he same recMque will k used to ansmct wmal networks for RCP Seal Failure cases. E m though we give a We for m e son of uncrnainiy assessmeat, a m w tbmugh anccnam . ty analysis would be desirable, if possible. Expcn system knowledge base formation and the integration of tbe prototype severe accident diagnostic sywm arc tbe rtmaiaiag tasks.

AQbJowLsxhlENT

m&aCC data mb SUffmCy b 8IK)vts a P p b t b

Tbe authors wish to thank Tam kcman and Dave Dion of Pacific Gas and Electric Company for useful discussions and tbc MAW results. lbis work is supponed in pan by grants from PGBUDOE and Soutbern California Edison Co.

REFERENCES

11) USNRC, Sowe Accidtnr R i s k An Assessmcnrjur Fiw US. Nucleor Power Planr.NUREG-1150, April, 1989

[Z] H a n ~ , DJ. et al., Accidcnr MOMgemcn~ hnjomrion Nee&, MTREG1CR.SS13, Apnl, 1990

131 Brownson, D.A. t t al., Inrentionat bepnssuritorion Accidcnr Managmunl Strategy,@ Pressurized W'arcr Teaaors. NuREGIcR-S937, Apil, 1993

BasedEJtpmSyJtemForSW~Acciden~ mnaguacnt", EPRI Proceedings, 1990

[SI Guano, S. et al., 'A I)ec&i Aid for Interpreting bstnnnmt 1d-m wd managing Accident Conditions in Nuclear Power Plant a* OECDKlSNl Specialists Meeting On htrmentation To Management Severe Accidents, Cologne, Germany, Marcb 1617,1992

[6J Xing, L. and olans D., "Application of N e d Networks in Accident Mrmaganmt", Roaedings of

IS] Si~m~in, E.B. pnb Kl~pp, G.T., Neural Network-

2%9

- --- -- ----

- - __-- -. - -- c---- - - _- - - - Florida, 1993 m ubrig, RE., "Ruauial Use of NcpnJ Networks in N ~ p o W a p l r a m t s " . l E E E l ~ ~ c o n f a n c e o n N e d Nerworkt, 1993

[SI Bosu, A. and Bprtlets E 8, "Detecdng Furlts in a Nuclear Powa P&mt by psing D p m k Node Arcbiteccon Artificial N e d Nenvodcr". NvctccV

PI MMcguura. M. and 230, E.. T d t Dbgmsis via Neural Netwwb: tbeBofmrumn Ibkbine".Nuc&ar

[lo] Specht, DS., 'Robabilistic Nemal Netwab". Neural Neworb, Vol. 3,1990

ill J Agogbo, A. et al, "Inttgmting N e d Networks witb IntluenccDiagramsforPotmtrpIantM~gd

Neural Nctwaks, 1993

Process Managemeat: A Chitical Review of Objectives, Constraints and Applicabk Tools". Reliability Engineering d System S@ty, N0.30, 1990

Roeess Disturbance Mana;gcmcnt The Models". Reliability Engineering and System Safety, N0.29, 1990

[14] Yang, J.O., Chang, S.H., " A Diagpostic Expert System for the Nuclear Powm Plant Based on the Hybrid Knowledge Appmadb". SEE€ T ~ t i o n s on Nuclear Science, Vo1.36, N0.6,1989

Potential for High Pressure Melt EJection resulting from a Suny Station Blackout Transient.

Accident Managcmcnt". Raxedings OfIntematioDal

W n C C Md EnginCCdqp, NO. 116,313-325,1994

Md Enginee?in& No. 117,19d200,1994

DiagDostics".3EEEIarematimalconfwwce~

[I21 GWIO, S.B., piagaosti~ Mtodels fWEagipeaing

[13] Kim, I.S. et al., "A Mobel-tlased Approach to m-fine

[15] budson, DL., Dobbc, C.A., Assesment offbe

NUREGXR-5949, May, 1993. [lq Oehlberg, RN. et aL, "RacW colrsidaations in

Meting 00 Safety Of Thmd ReacMs, 21-25,1991

JUlY

j17) Mantin Minsky and Seymour Papat, "Raceptrrms", Cambridge, MA: MIT Press, 1969.

Publishing Company, Inc.

Heidelberg 1990.

DataIDc.

[ 181 YOb-Han Pao, "Adaptive PsSWn Recognitio~ md Netaal Networks", Q@#kt 1 9 8 9 m - W ~ ~ l ~ y

1191 B. Miilla, f. Reinhardf "Nwral Networks, An

[ZO] "NEXPERT Object", Copyright 1987, by N e m

Introducti~n", Copyright 0 Springa-VH@ Beriin

290