Презентация на тему: " 1 Quality of PSA Data WORKSHOP on PSA Quality for Decision Making 16-20 February 2004, IAEA, Vienna, AUSTRIA Gennady Tokmachev." — Транскрипт:
1 Quality of PSA Data WORKSHOP on PSA Quality for Decision Making February 2004, IAEA, Vienna, AUSTRIA Gennady Tokmachev
2 Background PSA practice in Russia Experience from developing rules and procedures in Russia Results of regulatory reviews Results of multinational projects and their reviews Findings of IAEA IPSART missions Harmonization activity Documents being developed by IAEA, latest US NUREGs
3 Data processing task includes analysis of: Initiating event (IE) frequencies Equipment reliability data (i.e. operating failure rate, failure probability on demand and/or stand-by failure rate) Maintenance and test unavailability Common cause failure (CCF) data Special (conditioning) event data Comment. Data on human errors is usually derived separately within the HRA task and is not discussed in this presentation.
5 Terminology issues When PSA methodology came to Russia, some important terms had already been occupied in Regulatory documents and state standards. Examples. 1) Definitions of the following terms, namely «dependent failure» and «common cause failure», in Russian «General provisions of nuclear power stations safety …» OPB-88/97 GAN RF and definitions of the same terms used in PSA differ dramatically! Functional dependencies and internal hazards are covered by the definition of CCF. This contradicts to PSA practice. 2) Definition of frequency term used in PSAs is not supported by state standards on reliability, probability and statistics. Multi-language issues also appear when there is difference in languages between PSA producers and PSA users (e.g. between Russian and English)
6 Cost/benefit analysis of data processing Historically much more attention was paid to the collection of reliability data than data on IEs or CCFs Paradox: more efforts were spent on less important contributors, e.g.: –Reliability data - IAEA-TECDOC-478, 508, 636, 756(mainly), 930 –IE frequency - IAEA-TECDOC- 719, 749, 756 (a little) –CCF data - IAEA-TECDOC-648 CCFs usually contribute much more to CDF than independent failures IE frequency directly affects CDF Example. Increasing a frequency of one IE in 10 times and simultaneously reducing a frequency of another IE by factor of 10 can dramatically change a risk profile given the same model Current tendency. Last years comparison of PSAs for VVERs supported by IAEA and US DOE has focused on IE frequencies a lot
7 Initiating event frequencies Collection of specific data Problems if a data collection system is used: Event descriptions are not formalized for PSA purposes Potential underreporting. This is more relevant to shutdown PSA Not all IEs that occur at power result in a plant trip Several IEs can occur during a single plant disturbance: –One IE can completely cover another event. Discourage double counting (Example: reactor scram event followed by LOOP. Note. Dependent LOOPs are seldom evaluated) –One event is the direct sequence of another IE occurred early. (Example: inadvertent opening of BRU-A => closure of all MSIV). Need in coordination with AS modeling task Consequences of fires, floods, etc. have already been counted as an IE while performing the internal event PSA, e.g. FW rupture
8 Use of generic data in PSA Requirements of Russian Regulatory Rules It is not recommended to use only generic data in carrying out a Level 1 PSA for an operating NPP If generic data is used for, then source of information needs to be characterized Selection of generic data should be justified in terms of its applicability to the specific plant and equipment, component boundaries and failure modes defined in the Level 1 PSA If several sources of generic data are used then an approach to the selection of information should be presented and justified. Comment. Several sources can have the same origin Lessons learnt from IPSART missions: In using generic data both directly and for Bayesian updating, a week point is consistency between the generic data and the plant model (definition of initiating events, component boundaries, failure modes)
9 Initiating event frequencies Issues related to frequency estimation Uncertain information from sources of generic data: –Incompleteness of IE data in international DBs (e.g. IAEA IRS) Example from IAEA-TECDOC-749: 54 scram events at Paks NPP and only one event at Novovoronezh +Kola NPPs –Definitions of IEs in different PSAs or DBs differ considerably Examples: oSize of LOCAs oLoss of service water – a single train vs. the whole system, including suction water or not, loss of essential SW vs. non- essential one, etc.
10 Initiating event frequencies Issues related to frequency estimation (continued) Differences in design of NPP analyzed and other NPPs, e.g.: –Design of safety/relief valves (PSV, SGSV, BRU-A) –Configuration of support systems (e.g. 2/3/4 train SWS), etc. Impact of modifications –Algorithm of an accelerated unit unloading applied to VVER-1000 reduced dramatically the number of scram events in the nineties –Modification of BRU-A relief valves at VVER-1000 reduced dramatically the number of stuck open events after year 1989 Time trend –NUREG/CR-5750: a decreasing time trend was observed for many categories of events. –Results of fracture mechanics studies show an increasing time trend in LOCA frequencies over plant life time
11 Initiating event frequencies Comparison of data for medium LOCA
12 Initiating event frequencies Comparison of data for medium LOCA (continued) 1) Does design make difference? No. Primary pipes are the same, leaks via connections are restricted by throttles of D 32 mm 2) Does pipe steel make difference? No - in these particular estimates. Novovoronezh differs (stainless steel), but this was not taken into account in estimating 3) Does a leak size make difference? Leak size: –Novovoronezh mm –Kozloduy – mm –Bohunice – mm No, because the estimation method used ignores a leak size although there is the surge pipe of the pressuriser having D=200 mm (Bohunice) Conclusion 1. The same definition can mean IEs which are different
13 Initiating event frequencies Comparison of data for medium LOCA (continued) 4) Does specific data differ? No, zero event statistics and comparable plant specific exposures 5) Does estimation method differ? No, Bayesian method assuming a non-informative prior Answer: Difference in values was caused by different generic exposures assigned to zero event prior data: –Bohunice: f MLOCA = 0.5/115=4.3Е-3/a –Novovoronezh: f MLOCA =0.5/600=8.3Е-4/a Conclusion 2. The Bayesian method is very sensitive to the prior data Conclusion 3. There is a very high uncertainty associated with frequencies of sufficiently large breaks
14 Initiating event frequencies Comparison of generic data for LOCA (continued) Conclusion 4. The IE frequencies for rare events need to be rationalized. There is a need for an international consensus to make PSA results being comparable An attempt was made at an international workshop in Piestany, 2003: Worldwide experience for all PWR/VVERs shows that no such events have occurred to date in approximately 10,000 RY of operation. This suggests that a frequency of the order of 1.0E-4 per year could be justified as the sum of medium and large LOCAs Suggested values for for VVER-1000s are: –Large LOCA (200mm-800mm) - 3.2E-5/a (EF 10) –Medium LOCA (50mm-200mm) - 6.8E-5/a (EF 10)
15 First year of plant operation It is usual to eliminate data from the first year of plant operation since it represents failures that occurred during the plant break-in period. That is right for useful-lifetime of already operating plants: What about new plants? Their number will increase 1st year – highest risk Is is necessary to perform a PSA for trial operation of the unit? What regulatory criteria should be applied to?
16 Component reliability data Component boundaries and failure modes The coordination with system analysis is critical in many PSAs and surprisingly with AS and HRA analyses as well It is critical to define what the component boundary is to get the right data. Examples of typical errors in defining component boundaries: Example 1. A circuit breaker was included in the DG boundary considered in the PSA model. When deriving DG failure probability from data collected, failures of the DG circuit breaker were not counted. Example 2. Double counting of failures– e.g., relay - both in the boundary of pump and separately. Example 3. Failures resulting from human errors after testing, maintenance, and instrument miscalibrations were double counted in HEP (type 1) and component reliability
17 Component reliability data Causes of inconsistency between data and model Modification of NPP –Pre-modification data does not reflect the actual plant design –Typically, data prior to the design change is deleted from the data analysis because only post-modification operating history fully represents current conditions Event data logged by different workshops and separately identified in the plant maintenance records –For example, circuit breaker is typically included in the boundary of a pump, however its failures are often recorded by the electrical workshop rather than the reactor/turbine departments –Data collection procedure needs to be consistent with the model to avoid a potential for underreporting
18 Component reliability data Causes of inconsistency between data and model (continued) Use of manufacturers data –Differences in component boundaries, failure mode definitions, and conditions of manufactory tests, e.g.: Example of inconsistency: – The manufacturer's data for a "pump" failures typically includes only mechanical parts + the pump motor because other vendors are responsible for the other subcomponents –The PSA data for a "pump" includes, in addition, the motor circuit breaker, and parts of the control circuits that operate the circuit breaker – Finding: in a PSA for VVER the failure probability of the circuit breaker (subcomponent of the pump) > the total failure probability of the pump
the total failure probability of the pump">
19 Component reliability data Causes of inconsistency between data and model (continued) Testing mode is different from accident demands in terms of component boundary: –Partial testing, e.g.: idling diesel generator, i.e. some subcomponents included in the boundary of the component are not tested opening of relief valve without reverse –Short-term testing – e.g., fuel transfer pump can be included in the boundary of the diesel generator, but not started during a test of the diesel generator
20 Collection of plant specific reliability data. Issue -«improving» test results The first start fails followed by a successful start => the second start only is recorded in a test report An operator stops the test if the component is having trouble and does not consider the event as a test. Starts of components one by one before a complex test Careful inspection of equipment before a test Warming up DG before the test Manual initiation of MOV before opening by the motor NUREG/CR-6823: For PRA purposes, one is normally most interested in the failure probability on an actual unplanned demand. To estimate this, one should use only data from unplanned demands and from tests that closely mimic unplanned demands
21 Treatment of plant specific reliability data. Allocation of events Event allocation may affect dramatically the estimated values Requirement of Russian Regulatory Rules: Criteria for event allocation to failure modes and severity should be defined and justified Problems: –The requirements for recording the data may use definitions of severity and failure modes different from the PSA definitions. Need in translation –Sometimes maintenance records are so unclear that a definite classification of the severity of a component malfunction is impossible. First step - consultation with maintenance staff Conservative categorization of uncertain events => significantly higher failure probabilities Extrapolation of categorized events over unclear ones
22 Treatment of plant specific reliability data. Allocation of events (continued) Event categorization in terms of severity can be complicated if equipment is stopped by an operator: Example 1. Trip of a diesel generator due to unwanted hammering. Cause – a break of two bearings Example 2. Serial trip of two diesel generators due to surge Example 3. Pump trip after start due to a rapid temperature excursion in bearings If a degraded failure is revealed in a short test duration, an analyst cannot be sure the component would have succeeded over its mission time
23 Treatment of plant specific reliability data. Allocation of events (continued) Failure to start or failure to run? Usually in Russian PSAs failures of stand-by pumps, DGs, and fans discovered in 30 minutes after start are allocated to failure to start Actually such failures arise during stand-by period and are discovered with some delay, but far earlier than 24 hours Example. Pump trip in 10 minutes after start due to temperature excursion in bearings due to degradation of lubricating oil Easily, because periodical tests are usually lasted for 30 minutes Otherwise, failure rate to run – unrealistically high: there are some failure rates to run higher than 1Е-2/hrs Comment. T-book and some PSAs used another approach.
24 Treatment of plant specific reliability data. Unavailability due to unscheduled maintenance Unavailability due to corrective maintenance to be performed to eliminate degradation was missed in error in Procedures for conducting Level 1 PSA, 50-P-4, IAEA (1992), refer to Table XI. Only repairs caused by catastrophic failures are considered Coincident outage times for redundant equipment be permitted by TechSpecs –Should reflect actual plant experience => search while collecting raw data –Missing in the model => not collected –Operational feedback should be used for updating the model
25 Treatment of plant specific reliability data. Grouping of similar components/events Reason – operating history of a single component does not provide the number of events which is sufficient for estimation Goals of component/event grouping: –Increase of statistical significance –Reduction of efforts for data collection and treatment Drawbacks: –Masking trends and peculiarities –Extension of uncertainty due to differences in design –Potential for meaningless averages (e.g., circuit breaker + switch) Issue. No generally accepted approach to component grouping
26 Treatment of plant specific reliability data. Grouping of similar components/events Ideally many aspects should be the same, e.g.: –Component type/design/size/manufacturer –Operating mode (standby/operating) and environment –Frequency of demands/maintenance, etc. In practice some compromise is to be reached between statistical insignificance and inhomogeneity of the data by ignoring less significant criteria Approached used in PSAs are very different. It would be beneficial to develop a list of priorities for component grouping
27 Software reliability Computer based systems are of increasing importance to safety in NPPs In practice, expert judgment - to establish a subjective belief in failure probability of a specific software Russian Regulatory requirement (OPB-88/97): software reliability (SR) must be quantified IAEA Safety Guide NS-G-1.1 (2000): –Para Quantification of SR is an unresolved issue. Software testing has its limits and the quantification of SR for computer based systems may be difficult or impossible to demonstrate. –Para Currently available methods do not provide credible results => SG does not provide guidance on the use of SR models – Para Since software faults are systematic rather than random in nature, CMF of computer based safety systems employing redundant subsystems using identical copies of the software is a critical issue
28 Statistical methods What statistical methods are usually used in PSA? –Frequentist (classical) method –Bayesian method There is no perfect method. When pooling generic data or constructing prior distribution, expert judgment is the main source of uncertainty Sometimes there seems to be a gap between mathematicians and engineers responsible for data processing and mutual misunderstanding NUREG/CR-6813 (April 2003): PRA training and associated software tools to perform Bayes updating of generic distributions with plant specific data is surprisingly lacking. There is only one PSA software tool that has a built in capability to perform Bayes updating of distribution
29 Statistical methods. Bayesian approach The attitude to the Bayesian approach sometimes makes a religion of the method. As a result, no attention is paid to applicability of the generic data/Bayesian priors to the plant to be analyzed. Harmonization experience shows that differences in defining IEs are a major cause of variation in IE frequencies from one source to another Example from a PSA for NPP with VVER-1000 – specific data: 2 failures of HP ECCS pumps for 158 hours (2/158=1.3E-2/hr) –Bayesian procedure was used with a huge bulk of the US prior data. –Posterior failure rate – 2. 13E-5/ hr with EF of 3. –Statistical uncertainty was only considered, but a variability of the parameter due to differences in design was not taken into account
30 Plant specific data vs. combination When events are recorded the statistical significance can be measured by the range from the 5th to the 95th percentile confidence bounds For a Poisson distribution, this range is on the order of 10, with 2-3 events. Russian practice: For 3 events and more the sample is very loosely comparable to the lognormal with an EF of 3 => to use only plant specific data. This three event rule - to make decision whether generic data should be involved in parameter estimation
31 Common cause failures Is it necessary to improve CCF data in PSA? CCFs can contribute significantly to the overall risk from NPP, usually much more that independent failures Paradox 1 – reviews usually provide far more comments on reliability data Paradox 2 - CCF parameters are usually estimated statistically based on operational experience associated with one-off type events Ignoring CCFs => Example from the reliability calculation of a scram system designed for VVER Probability of system failure < 1Е-300 Operational experience - Surry NPP in USA: multiple hanging of control rods due to lubricant solidification in scram breakers. CCFs – the measure of our ignorance Parameters of CCF models – the result of consensus
32 Data analysis Special event probabilities As a rule - not considered in rules and procedures Often - not documented well in PSAs Special events: Typically, apart from IEs, failures, unavailabilities, HEs Included in the probabilistic model Characterized by probabilities Define possibility of some special conditions or system configurations Component failures which can not be quantified using statistical approach
33 Data analysis Special event probabilities Examples of some special events: Clogging of the containment sump (safety injection pumps) by primary thermal insulation following a break of a reactor coolant pipe Reactor pressure vessel rupture given an overcooling System configuration: Grid breakdown due to unit disconnection Break in a non-isolable part of the primary circuit (use of MIVs at some plants) Specific cause of a scram actuation (signal structure for different IEs) Influence of the adjacent unit Impact of steam floods, etc. Do we need guidance to treat them?
34 Conclusions Guidance on data processing needs to be more specific: NUREG/CR is a good example Special attention should be paid to coordination with other PSA tasks and plant staff involved in data collection/treatment Frequencies of large/medium LOCAs need to be harmonized Is it necessary to perform a PSA for trial operation of a unit? Event allocation is a week point of many PSAs Is it necessary to develop a PSA software tool that has a built-in capability to perform Bayes updating or something like that? Criterion for use only plant specific data to avoid expert judgment? There is a need in data on software reliability CCF data. Have we reached perfection? Special events. Do we need guidance to treat them?