A computer based living probabilistic safety assessment (LPSA) method for nuclear power plants

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<ul><li><p>Nuclear Engineering and Design 265 (2013) 765 771</p><p>Contents lists available at ScienceDirect</p><p>Nuclear Engineering and Design</p><p>j ourna l h om epa ge: www.elsev ier .com/</p><p>A comp se(LPSA) </p><p>MuhammIftikhar Aa Department o ic of Kob College of Nuc Department od College of Mae Key Laborator ongqi</p><p>h i g h l </p><p> A computer based LPSA method named, online risk monitor system (ORMS) has been proposed. The essential features and functions of ORMS have been described. A case study of emergency diesel generator (EDG) of Daya Bay NPP had carried out. By using ORMS operational failure rate and demand failure probability of EDG has been calculated.</p><p>a r t i c l</p><p>Article history:Received 19 FeReceived in re10 SeptemberAccepted 12 S</p><p>1. Introdu</p><p>Over thezations havenhance thtool becausplant safetyhave a defetory body aapproach to</p><p>In the paanalysis anand fault trefor the analcation and f</p><p> Corresponversity of Engi</p><p>E-mail add</p><p>0029-5493/$ http://dx.doi.o e i n f o</p><p>bruary 2013vised form</p><p> 2013eptember 2013</p><p>a b s t r a c t</p><p>To update PSA (probabilistic safety assessment) model this paper presents a computer based living prob-abilistic safety assessment (LPSA) method named as online risk monitor system (ORMS). The essentialfeatures and functions of ORMS have been described in this research. A case study of emergency dieselgenerator (EDG) of Daya Bay nuclear power plant (NPP) has been done; operational failure rate anddemand failure probability of EDG has been calculated with the help of ORMS. The results of ORMSare well matched with data obtained from Daya Bay NPP. ORMS is capable of automatically update theonline risk models and reliability parameters of equipment in time. ORMS can support in decision makingprocess of operator and manager in nuclear power plant.</p><p> 2013 Elsevier B.V. All rights reserved.</p><p>ction</p><p> past years, many nuclear power plant (NPP) organi-e performed probabilistic safety assessments (PSAs) toe safety level of NPP. These PSA studies is an effectivee it assist plant management to get more benets for</p><p> but any PSA used to support decision making mustnsible basis therefore it is very important that regula-ccept Living PSA. LPSA provides basis for risk informed</p><p> decision making.st (Balfanz et al., 1992) suggested a system named safetyd information system (SAIS) to investigate event treee construction. Poucet (1990) developed software tool</p><p>ysis of reliability and safety (STARS) for hazard identi-or logic model construction. Even if these systems were</p><p>ding author at: Department of Basic Sciences and Humanities, Uni-neering and Technology Taxila, Pakistan. Tel.: +923348610154.ress: zubairheu@gmail.com (M. Zubair).</p><p>computer based but most of the work needs to be done manually.On other hand ORMS presented in this study is capable to updatereliability data and failure rates in a quick manner by using Bayestheorem.</p><p>In safety analysis, the prime objective is to identify thepotential sources of system failure. These sources belong to thecomponents, process materials, operating procedures, workingpersonnel, process instrumentation, etc. Apart from general engi-neering evaluation, several techniques have been developed forthe identication of potential sources of failures and provideuseful information for fault tree analysis (FTA) and event treeanalysis (ET). Some of the famous techniques include check-lists, preliminary hazard analysis (PHA), failure mode and effectanalysis (FMEA), hazard and operability study (HAZOP), masterlogic diagram (MLD), etc. Some of these procedures have beenimplemented using computers in a much easier, convenient andinteractive way and in some programs there is provision to per-form two or more types of analyses jointly (Kumamoto et al., 1996;Crowl et al., 2002; Venkatasubramanian et al., 1994; Sang et al.,2010).</p><p> see front matter 2013 Elsevier B.V. All rights reserved.rg/10.1016/j.nucengdes.2013.09.017uter based living probabilistic safety asmethod for nuclear power plants</p><p>ad Zubaira,c,, Zhang Zhijianb, Gyunyoung Heoa,hmedd, Muhammad Aamire</p><p>f Nuclear Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 446-701, Republclear Science and Technology, Harbin Engineering University, PR Chinaf Basic Sciences, University of Engineering and Technology, Taxila, Pakistanthematics and Statics, Chongqing University, 401331, PR Chinay of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ch</p><p>i g h t slocate /nucengdes</p><p>ssment</p><p>rea</p><p>ng 400030, China</p></li><li><p>766 M. Zubair et al. / Nuclear Engineering and Design 265 (2013) 765 771</p><p>Nomenclature</p><p>n number of failuresk number of demands</p><p> shape parameterpost posterior value of shape parameterprior prior value of shape parameter</p><p> scale parameterpost posterior value of scale parameterprior prior value of scale parametert timeAOT allowed outage timeEFS experience feedback systemd downtime associated with an AOTf downtime frequency or the average yearly fre-</p><p>quency of occurrences of the AOTR1 the increased risk level, e.g., increased CDF, when</p><p>the component is known to be down or unavailableR0</p><p>R </p><p>r Ry T1 </p><p>T2 </p><p>A risk mdetermine tsystems anreects thetus of the vare any comsafety monrisk monitomonitors ware used forLarge earlyas safety fumeasures li</p><p> Baseline retc.) calcuout their </p><p> The average risk which is normally calculated by the Living PSAfor full power operation. Average risk is calculated when averagemaintenance unavailability Introduced and it is always greaterthan the baseline risk.</p><p> The pointplant. Thuration a(NEA/CSN</p><p>In this rOn the basthis article and reliabilinternal evautomatica</p><p>2. Method (OR</p><p>SAs, eled tem ainseen </p><p> for aventer, w, the lsic evduct</p><p> whicondlle o</p><p>mbintionthe reduced risk level, e.g., reduced CDF, when thecomponent is not down, i.e., down unavailability iszerothe increase in the conditional risk level, e.g.,increase in CDF, given the component is downsingle-event AOT riskyearly AOT riskfailure rateallowed conguration time as the rst type of oper-ational event happenallowed conguration time as the second type ofoperational event happen</p><p>onitor is a plant specic real time analysis tool used tohe instantaneous risk based on the actual status of thed components. At any given time, the safety monitor</p><p>system</p><p>In Pis modthe sysdant trhave bof thisbasic eHowevor testthe baThis remodel</p><p>Seca moduand cocalcula current plant conguration in terms of the known sta-arious systems and/or components, e.g. whether thereponents out of service for maintenance or tests. The</p><p>itor model is based on the LPSA (IAEA, 1999). The rstrs were put into operation in 1988. The number of riskorldwide has increased to over 150. The risk monitors</p><p> quantitative analysis like core damage frequency (CDF), release frequency (LERF) and qualitative analysis suchnction, safety system. There are different types of riskke;</p><p>isk which is the numerical value of the risk (CDF, LERF,lated by the PSA with all components available to carrysafety function.</p><p>Fig. 1. Average, baseline and point in time risk.</p><p>ator just neto RDUM, aautomaticato recalculathese value</p><p>The basias;</p><p> Reliability Running t Redundan Engineere General s</p><p>These RDUM and(digital insttatively andthree modudesign chantive and quawith reliabrespectivela quick calc</p><p> Core dam Importan Allowed c Qualitativ-in-time risk is the level of risk is related to a specice point-in-time risk will change as the plant cong-nd environmental factors change as shown in Fig. 1I/R, 1996).</p><p>esearch a methodology for LPSA has been developed.ics of this methodology ORMS has been presented inwhich is capable to calculate changes in congurationity of components in NPP. ORMS is based on full power,ent Level 1 PSA and update risk models regularly andlly.</p><p>ology and structure of online risk monitorMS)</p><p>modication of systems with a high level of redundancyat a system level. This is done by adding basic events tofault trees or Bayesian Network to model all the redun-</p><p> and these basic events have xed probabilities whichdetermined using a -factor approach. As an example</p><p> three train system, the system is modeled as a single which represents failure of 3 out of 3 redundant trains.hen a train of the system is removed for maintenanceevel of redundancy is reduced to a two train system andent needs to be reduced to failure of 2 out of 2 trains.ion in redundancy is recognized in the part of the PSAh represents random failures.y, the reliability data update module (RDUM), which isf ORMS, work in such a way that it uses Bayes Theoremation of different distributions (beta and gamma) for the</p><p> and updating of parameters. As failures occur then oper-ed to recognize these failures and provides these valuess a result parameter values automatically updated. Herelly means that there is no need for operator or expertte these values instead ORMS has capability to updates.c methodology of ORMS consists of ve modules known</p><p> data update module (RDUM)ime updatet system unavailability updated safety function (ESF) unavailability updateystem update</p><p>ve modules are shown in Fig. 2. The rst two modules running time update receive information from DI&amp;Crumentation and control) system, analyzed data quanti-</p><p> supply feed back to reliability data base. The remainingles receive information from monitoring unit &amp; systemge unit and analyzed data qualitatively. The qualita-ntitative output of these three modules in combination</p><p>ility data base module is provided to Living PSA model,y. After getting information the online risk model makesulation of following factors;</p><p>age frequencyce factoronguration timee risk information</p></li><li><p>M. Zubair et al. / Nuclear Engineering and Design 265 (2013) 765 771 767</p><p>Reliability DataBase</p><p>Living PSA Model</p><p>Online-Risk modelcalculation</p><p>Reduuna</p><p>ESF U</p><p>Gener</p><p>RDUM</p><p>Running TimeU</p><p>Record -Unit</p><p>Record-Unit</p><p>S.</p><p>In view to shut dowing processto I&amp;C systFig. 3 descrThe reliabilare calculat</p><p>To prevgathered antion based, criticality acomparisonprogram annance prog</p><p>3. Specic</p><p>3.1. RDUM </p><p>The RDUand combinupdating oftypes of dis</p><p>(1) Beta dicalculatexplain</p><p>RDUM</p><p>es The</p><p>Gamma and PoissonDistribution</p><p> of combinestribution</p><p>Beta and BinomialDistribution</p><p>Calculation andUpdating of parameters</p><p>Fig. 4. Function of RDUM.</p><p>cess or steps can be seen as described by Zubair and Zhijian11).</p><p>st = k + prior (1)</p><p>= n k + (2)Over-Risk limit</p><p>Shut Down</p><p>RCMYes</p><p>No</p><p>Fig. 2. Structure of ORM</p><p>of calculation, the online risk model makes it possiblen plant if risk exceed over a limit and continue updat-</p><p> if risk levels liaise within limits. The online assessingem and getting the conguration information of NPP.ibes the automatically updating of online risk models.ity parameters of the equipment and other informationed at this stage.ent failures reliability centered maintenance (RCM)d compares all updated data for analysis. RCM is condi-with maintenance intervals based on actual equipmentnd performance data (IAEA, 2007). The purpose of</p><p> in RCM is to identify needed changes in the existingd thereby optimize the facilitys preventive mainte-</p><p>ram.</p><p>ation of modules in ORMS</p><p>and running time update</p><p>Bay</p><p>Usedi</p><p>pro(20</p><p>po</p><p>M work in such a way that it uses Bayes Theoremation of different distributions for the calculation and</p><p> parameters, Fig. 4 describe this concept clearly. Twotributions have been used.</p><p>stribution with binomial likelihood function for theion of demand failure probability. Eqs. (1) and (2)</p><p> the key results of these distributions and the calculation</p><p>Fig. 3. ORMS online features.</p><p>post</p><p>(2) Gammarunning</p><p>post = </p><p>post = </p><p>The updmodel requthe passageity of compto update mtion of updaRDUM makthen RDUMtwo minute</p><p>Table 1 event happrst type ocongurationdant Sys.vailabilityupdate</p><p>nav. update</p><p>al sys update</p><p>D-I&amp;C</p><p>pdate</p><p>OSS</p><p>Monitoring unit</p><p>Monitoring unit</p><p>Sys. Designchange</p><p>oremprior</p><p> distribution with Poisson likelihood function to update time, Eqs. (3) and (4) explain nal results.</p><p>x + prior (3)</p><p>t + prior (4)</p><p>ating of parameters can be achieved according to PSAirement. The failure rates of components changes with</p><p> of time. So to represent these changes and unavailabil-onents become a question mark for safety engineersodels as a result RDUM start its operation till comple-ting. If there are no changes in plant conguration thene one calculation per hour, but if congurations changes</p><p> starts immediately and makes calculation once everys in one hour.represents allowed conguration time as operationalens, where T1 denotes allowed conguration time as thef operational event happen and T2 represents Allowedn time as the second type of operational event happen.</p></li><li><p>768 M. Zubair et al. / Nuclear Engineering and Design 265 (2013) 765 771</p><p>Table 1Allowed conguration time as operational event happen.</p><p>T2/T1 T2 8 h 8 h T2 24 h T2 24 hT1 8 h 1 h 1 h 1 h8 h T1 24 h 1 h 8 h 8 hT1 24 h 1 h 8 h 24 h</p><p>Determine theCondition of</p><p>Severe accident</p><p>Plant InternalInformation</p><p>Availability ofInstruments</p><p>Monitoring</p><p>Fault ProtectionDevice (Valve or</p><p>Pump)Signal</p><p>GenerationTransmitter</p><p>Decision</p><p>3.2. Redund</p><p>The funanalysis of redundant unit, whilesystem desshown in Fthat it reecbased on thsevere accidditions, usethis is availmodeling te</p><p>There ar</p><p>(i) Signal judgmeof com</p><p>(ii) Succespressur</p><p>(iii) NormaThe Fauprobab</p><p>The sysincreased r</p><p>component assumed down or the component unavailability equalto 1 (NURGE/CR-6141, 1994). R0 is the reduced CDF with the com-ponent assumed up, i.e. the component unavailability equal to zero(means component available). In terms of R1 and R0 the increase Rin risk level associated with the allowed outage time (AOT) then;</p><p>R = R1 R0Using the above expression, the single-event AOT risk and the</p><p>yearly AOT risk can be expressedas, r = single-event AOT risk= (R1 R0) dAndRy = yearly AOT risk contribution= f.r= f. (R1 R0) dR1 can be calculated by setting the component down event to a</p><p>true state in the PSA. Similarly, R0 can be calculated by setting thecomponent down event to a false state in the PSA.</p><p>The AOTs for components and system trains are the times givenlant technical specications for typical/bounding plant con-ions planon deformsk anutindes.</p><p>e stu</p><p> daty 199</p><p> Tab equck sytor sndengeneted f</p><p> to boing e</p><p>o dieener</p><p>G fa</p><p>Table 2Ten years dat</p><p>Time (years)</p><p>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 System</p><p>Fig. 5. Logical conguration of monitoring system.</p><p>ant, ESF and general system unavailability update</p><p>ction of these three modules is to make qualitativedata and provide this information to LPSA model. Theand ESF modules receive information from monitoring</p><p> general system module updated as changes occur inign. The logical conguration of monitoring system isig. 5. In ORMS system updated module is necessary sots the current design and operation of the plant which ise most up to date analysis (thermal-hydraulic analysis,ent analysis, etc.) of how the plant behaves in fault con-s data derived from plant operating experience whereable and takes account...</p></li></ul>