"PSA AS A TOOL FOR EVALUATION OF AGEING EFFECTS ON THE SAFETY OF NPPs" Andrei Rodionov

October 17, 2008 1

"PSA AS A TOOL FOR EVALUATION OF AGEING EFFECTS ON

THE SAFETY OF NPPs"

Andrei Rodionov

October 17, 2008 2

1. INTRODUCTION : PSA Application in the Long-Term Operation of NPPs Rationale of the Ageing PSA Network

2. SELECTION OF SYSTEMS, STRUCTURES AND COMPONENTS SENSITIVE TO AGEINGProposed ApproachRelated Uncertainties and Limitations

3. ACTIVE COMPONENTS RELIABILITY ASSESSMENTTask Specification: Incorporation of Ageing Effects into the PSA ModelTime-Dependent Reliability ModelsData issues

4. IMPACT OF AGEING EFFECTS ON THE SYSTEM AND PLANT LEVELSApproach Proposed for DemonstrationSystem Level Effect Plant Level Effect

5. CONCLUSIONS. (+Summary of activities and future planning)

October 17, 2008 3

INTRODUCTION

Motivations : • NPPs are getting older• Probabilistic safety goals have to be maintained for the LTO• Application of Risk Informed RegulationQuestions :• Could PSA be applied to ageing assessments? • How realistically do PSA models reflect important ageing issues?• Are any modifications or revisions of PSA assumptions needed to apply

a PSA approach to risk-informed decision-making with regard to ageing evaluation?

• What data are available and how representative are they with regard to important ageing issues?

October 17, 2008 4

Background and motivations

Actual situation worldwide:• 213 (49%) are between 20 and 30 years old,• 115 (26%) units are between 30 and 40 years

old,

In total : about 3/4 of the 438 reactors Long-term operation : on July 2006, 1/2 of the

licensed NPPs in US had received or were under review for license renewal

Situation in Europe (EU):• 88 (60%) reactors from 146 are in the age

of 20-30 years old • 24 (16%) between 30 and 40 years old

Europe - # of reactors per age. March 1, 2008.

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2122 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 38 40

Age of reactors

October 17, 2008 5


VVER reactors in EU MS (19 reactors) : about the same ages distribution.* RF -15 and UA – 15 reactors in operation

EU MS - Prevision for 2018 19 (14%) reactors from 139 need to be

licensed for life extension (LTO) or shutdown,

94 (67%) have to be under evaluation for life extension or definitive shutdown.

**supposing that Sweden and Germany will continuer NPPs operation only up to the end of design lifetime (40 years) and 4 new reactors will be

commissioned.

# of VVER in Europe (total 19)

0

2

4

6

8

10

12

0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40

Age of reactor

Series1

Europe - Prevision for 2018

0

2

4

6

8

10

12

14

16

1 4 7

10

13

16

19

22

25

28

31

34

37

40

43

46

49

Age of reactors

new reactors presently in operation LTO desision ?

October 17, 2008 6

background and motivations

Probabilistic Safety Goal (INSAG-12) : “The target for existing nuclear power plants consistent with the technical safety objective is a frequency of occurrence of severe core damage that is below about 10–4 events per plant operating year. Severe accident management and mitigation measures could reduce by a factor of at least ten the probability of large off-site releases requiring short term off-site response.”

Risk Informed Regulation

1. Define change

2. Perform engineering

analysis

3. Define implementation / monitoring program

4. Submit a proposal

PSATraditional analysis

October 17, 2008 7


RISK BARRIER TARGET

Operating loads

+

Environmental stressors

SSC degradation due to the

ageing

Design

Qualification

Test/Monitoring

Maintenance

Operating Feedback

SSC Reliability/availability

Risk to overal plant safety

Ageing effect on unit/SSC reliability and safety

Main activities related to the ageing management :• Ageing management • Life time extension • Surveillance and Maintenance optimisation

October 17, 2008 8


Current situation Standard PSA tools do not adequately address important ageing issues as :

– assumption on component reliability model =const, – reliability data may not adequately represent the current status of the plants, – existing PSAs overlook some components (e.g. cables, structures, etc.) as

having a very low failure probability, but they may have increasing weight due to ageing effects

Limited experience with applying of PSA approach to evaluate ageing effects :– no commonly accepted methods, – all the studies are performed by relatively isolated organisations, – and publications on the subject are scarce

October 17, 2008 9

Needs for Advances Time-Dependent Reliability Analysis

Possible impact to PSA resultsPassive components

IE frequencies and definitions (high Risk Importance) Safety Systems unavailability due to the failures of non-redundant parts

Active components Safety Systems unavailability due to the ageing failures, Safety Systems unavailability due to the unplanned maintenance, IE frequencies (when modeled with a FT), Intersystem CCF

Practical implications Risk Profile and RI factors used as decision criteria in many applications could be changed in time. These changes have to be considered in decision making process.

October 17, 2008 10

EC JRC Network on Aging PSA : goals, tasks, working approach

JRC Ageing PSA Network Objectives :

Using common resources of Network participants to identify, develop and demonstrate methods and approaches which could help PSA developers and users

– to promote the use of PSA for ageing management and risk-informed applications of Nuclear Power Plants in LTO,

– to incorporate the effects of equipment ageing into current PSA models to perform engineering analysis,

– in case where age-dependent PSA couldn’t be applied (absence or non-adequacy of ageing probabilistic model, lack of data, etc.), to specify and prioritize reliability monitoring actions/approach to assure that potential decreasing of reliability of SSC would be identified and corrected in time

October 17, 2008 11


Main tasks : (Terms of references EUR 22645 EN )

• Task 1. Organization and coordination of network activities • Task 2. Analysis of main PSA tasks with regards to Ageing PSA (in

progress)• Task 3. Selection of the SSC to be considered in Ageing PSA (in

progress)• Task 4. Reliability and data analysis for active components (in

progress) • Task 5. Reliability and data analysis for active components II.

Common Cause Failures (started)• Task 6. Reliability and data analysis for passive components (started)• Task 7. Incorporation of age-depended reliability parameters and data

into PSA model (in progress) • Task 8. Ageing PSA development and applications (started)

October 17, 2008 12


Participants : • 15 organizations from EC MS, Swiss, RF, Armenia and Korea (network agreement).

In addition, active participation from CNSC/Canada, D.Kelly and C.Atwod/USA

Network Operation : • Working groups • Case studies and benchmark exercises,• Steering Committee, • Expert meetings,• Workshops • Web site : http://safelife.jrc.nl/APSA

Steering Committee Chairmen : M.Patrik

Operating Agent : JRC Network Coordinator : A.Rodionov (Task 1)

TG for Task 2 Task leader :

M.Patrik (NRI/CZ)

Participants : - JRC - INR/RO - JSI/SI ? - NRSC/AM - VEIKI/HU

TG for Task 3 Task leader : M.Nitoi (INR/RO) Participants : - JRC - CNE/RO - VEIKI/HU - NRI/CZ - NRSC/AM - IRSN* - CNSC*

TG for Task 4 Task leader : A.Rodionov(JRC) Participants : - INPE/RF - CNE/RO - LEI/LT - JSI/SI ? - IRSN* - CNSC* - VEIKI*

TG for Task 7 Task leader : D.Serbanescu (JRC) Participants : - IRSN - CNE/RO - LEI/LT - JSI/SI - ENEA/IT - TUS/BG

http://safelife.jrc.nl/APSA

October 17, 2008 13





5. CONCLUSIONS

October 17, 2008 14

SELECTION OF SSCs SENSITIVE TO AGEING

Current Ageing Management practice : • often limited to passive components• if not based on expert judgments

Proposed Approach includes three elements :• prioritize the SSCs which are modeled in PSA using RIF,• perform trend analysis of available reliability data,• use qualitative assessment or Ageing Failure Modes and Effect

Analysis (AFMEA) for a limited number of componentsAll three steps are consecutive and complementary.

Component modeled in PSA

Risk Importance Reliability data

Trend analysis AFMEA

SelectRank 7

No

SelectRank 6

No

Reliability data

Trend analysis

SelectRank 5

No

SelectRank 3

No

AFMEA

Reliability data

Trend analysis AFMEA

SelectRank 2

SelectRank 1 NoAFMEA

SelectRank 4 No

Selection process

October 17, 2008 16


Decision matrix for selection of SSCs susceptible for ageing

Rank Risk Importance Trend in failure rate

AFMEA conclusion

1 High Identified -

2 High - Sensitive

3 High Absent Sensitive

4 Low Identified -

5 Low - Sensitive

6 - Identified -

7 - - Sensitive

October 17, 2008 17


Related Uncertainties and Limitations

Prioritization by risk importance limitations: • (+) permits significantly reduce the list of SSCs, • (-) it deals only with SSCs modeled in PSA,• (-) importance of particular SSCs could be changed with time due to the ageing,Trend analysis • (+) direct indication of ageing,• (+) provide the basis for modelling,• (-) lack of data,Qualitative assessment • (+) could cover SSCs initially neglected in PSA and those for which there is not

enough failure data for trend analysis,• (-) very time-consuming and resource-intensive,• (-) qualitative results

October 17, 2008 18





5. CONCLUSIONS

October 17, 2008 19

ACTIVE COMPONENTS RELIABILITY ASSESSMENT

Integration of SSC ageing model to PSA• time-dependent reliability parameters• PSA model• CDF at different age-points(10, 20, 30 and 40 years of operation)

Time-dependent reliability model :• collect and process reliability data, • fit one of the parametrical models and calculate the model parameters, • if necessary, improve the results by Bayesian approach and generic data, • evaluate the unavailability factors for given age values, taking into account

model parameters, periodic tests and maintenance data

(t)

[ti, ti+1] t

av,

i

October 17, 2008 20

Time-Dependent Reliability Models

Choose the model – Constant failure intensity (rate) : = const– Linear : – Log-linear or exponential : – Power-low (Weibull) :

2 > 0 means a positive trend in time

The procedure for choosing the model and parameters includes the following steps: – verification of model validity,– parameter estimation,– characterization of uncertainties of estimated parameters and the whole model,– assessment of possible extrapolation and uncertainties of extrapolation

;t

1 2;t t

1 2ln ; t t

21;t t

October 17, 2008 21


The following issues require specific attention when analyzing and interpreting the results:– relative increase in failure

intensity (rate) in time with regard to constant failure rate,

– impact of burn-in failures,– uncertainties of extrapolation

October 17, 2008 22


Component

group

Best

fitted

model

Parameters

:

p-value

=c =c =c =c

0.005

#32.2 const

0.54

0.005 1 1 1

0.0018

0.49

#32.2 /early

failures

excluded

Weibull

0.65

0.005 1.11 1.56 1.91

0.013

0.11 #39.1

log-

linear

0.07

0.067 0.58 1.75 5.26

0.0032

0.18

#39.1/early

failures

excluded

Log-

linear

0.14

0.075 0.26 1.56 9.45

0.0035

-0.65

#48.3 Weibull

0.54

0.0011 0.72 0.46 0.35

0.00021

0.081

#48.3/early

failures

excluded

Log-

liner

0.997

0.00064 0.74 1.66 3.74

October 17, 2008 23

Data issues

Possible sources of data :• reliability data for NPPs (generic and/or specific),• reliability data for similar components from other industries,• accelerated ageing reliability tests

Reliability data from fossil plants 20-40 years old (NERC and VGB data)

Main conclusionsIn general (good news), with age plants “learn” : availability and performance increase

or maintained constant with the age of the plant

Particular cases of ageing : • increasing failure rates are identified for several types of components as pumps,

funs, valves, switchers, boilers, heaters, etc. ( ageing parameters for linear aging model are comparable with TRIGALEX data );

• “minimal repair” maintenance strategy could lead to increase in failure rate;• even with decreasing or constant failure frequency, reliability of component could

degrade with age because of increasing of repair frequency and its duration

October 17, 2008 24

Activities and results : Reliability and data analysis for active components (Task 4)

October 17, 2008 25

Data issues

Reliability data for NPPs: (questionnaire on data availability andaccessibility - conclusions EUR23084EN)PSA reliability data - well structured and of high

quality,- easily accessible (DB or electronic

docs),- not sufficient for ageing

assessments =const => n + t)

Additional data has to be extractedand processed from raw data

PSA Reliability DB

IE frequencies Component reliability parameters

ProcessedOperational Event data

Processed component failure, performance and maintenance data

Raw data sources

Operating (defect) and maintenance logs

Operating and maintenance procedures

Design, commissioning and manufacturing information

Abnormal Operational Events (LER) Reporting System

Generic data (Reliability parameters and IE frequencies)

Other reliability DBs (vendor DB, NDE of piping systems DB, I&C elements DB, etc.)

October 17, 2008 26

Data issues

Nomenclature of PSA Component Reliability Data

Reliability parameters (2a)1. Component group2. Failure modes3. Parameters : •Failure rate,•Failure prob. per demand,•Mean time to repair,•Unavailability due to the maintenance.4. Uncertainty

Data for parameters estimation1. Component group2. Component description / limits3. Operating mode 4. Component function5. Failure modes6. Failure criteria7. Sample (# and list of components)8. Observation period9. Stressors (av. # of hrs or d per year)10. Cumulated stressors11. # of failures per failure mode12. Estimation method / assumptions

Data for cumulated operation time13. Component ID (sub-group)14. # of compon. in sub-group15. # of hrs or demands / year16. Observation period17. Total cumulated stressors

Failure data18. Unit / component ID19. Failure date20. Reactor state21. Failure mode22. Criticality factor23. Repair time24. Failure cause / description

Processed data (2b)

October 17, 2008 27

Data issues

Age-dependent reliability models : 1) simple models or trend assessment; 2) models which include test and maintenance evaluations; 3) comprehensive models

Categories of additional data needed:Type 1 models• component commissioning date,• failure/censoring times,• component replacement date.Type 2 models - data listed for Type 1, plus: • tests and maintenance strategy

– type and periodicity,• degree of component renewal (CM and PM)In fact these data needed for T1 models to make the assumptions about renewal processType 3 - data listed for Type 1 and Type 2, plus:• component lifetime,• cumulative number of hours in operation, number of demands,• average and extreme levels of operating

and environmental stressors

Availability and accessibility of data for different types of reliability models

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Type 1 Type 2 Type 3

K1 - availability K2 - accessibility

October 17, 2008 28

Data issues

Main conclusions :• Processed data about failures and component performance could be certainly used

for age-dependent reliability analysis, but is not enough for this purpose • PSA reliability data collection process does not include any requirement to perform

a statistical validation of assumptions about constant failure rate or trend analysis • Improving reliability data collection could greatly help with PSA and age-dependent

reliability analysis applications in risk-informed decision making process

October 17, 2008 29

Needs for Advances Time-Dependent Reliability Analysis

Practical implications : • reliability analysis of ageing trend has to be a part of the process.

Experience of application• analysis of ageing trends (large populations) :

(?) data collection covers only risk important components => no data available for all components potentially susceptible to ageing; also for many components statistical data are not enough,

Typical results of analysis :• ~ 30% of analysis cases : not enough failure statistic (with sufficient

demands/operating times) = very reliable, no ageing• ~ 20-25% of analysis cases : constant failure rate = no ageing, • ~ 5-15% of analysis cases : decreasing failure rate – after burn-in period and after

modifications– What have to be considered as parameters?

• ~ 20-30% of analysis cases : increasing failure rates – detailed engineering investigation is required before conclusion about ageing

– ??? issues

October 17, 2008 30





5. CONCLUSIONS

October 17, 2008 31

IMPACT ON THE SYSTEM AND PLANT LEVELS

• To calculate the impact of ageing on the risk profile as a function of unit age, it was proposed to use CDF as the average value at one-year intervals calculated for different age points, for example for 10, 20, 30 and 40 years in operation

• A three-loop PWR PSA model for a Large LOCA initiating event was considered for this purpose. The model consists of 4 Event Trees developed for full power operation and hot shutdown reactor states

• The set of “virtual” reliability data was prepared on the basis of the results of case studies, available generic data sources and expert opinions. The data includes time-dependent reliability models for certain mechanical, electrical and I&C components of Low Pressure Safety Injection (LPSI) and Containment Spray (CSS) Systems

• “As bad as old” preventive maintenance was considered in all cases

• Quantification has been calculated for a reference value (no ageing effects considered) and age points of 10, 20, 30 and 40 years

October 17, 2008 32

Relative increasing in failure rate

0.00

1.00

2.00

3.00

4.00

5.00

6.00

10 20 30 40

Age

RWST level sensors

CSS pumps

Relative increasing of failure probability

0.10

1.00

10.00

100.00

1000.00

10000.00

10 20 30 40

Age

CSS pump's motors(log linear)

pump's motors(Weibull)

Examples of component reliability data

October 17, 2008 33

System Level Effect

Results of calculations for CSS:• Unavailability increases with time by > than

one order of magnitude with regard to the reference value (see Fig. 3)

• Up to 30, the main contributor to unavailability is the failure of level sensors in the RWST But at the age of 40 the dominant impact on system unavailability is failure of the CSS pump motors

• “No ageing” contributors : HF - human error (20% fractional contribution to the reference value) and CSS pumps fail to start (2% contribution to the reference value)

CSS unavailability

0.00E+001.00E-032.00E-03

3.00E-034.00E-035.00E-036.00E-03

7.00E-038.00E-039.00E-03

10 20 30 40

Age

Ref. value

Q(t)

Fractional contribution to CSS unavailability

0

10

20

30

40

50

60

10 20 30 40

Age

%

HF

MOVs onsump lineLS

Pump FS

Pump FR

Pump Motor

October 17, 2008 34

Plant Level Effect

Impact of SSC ageing on CDF

• CDF increases from 6.58E-8 at 10 years to 6.19E-7 at 40 years. In comparison with the reference value the increase is by a factor of 8.6 by the end of the designed lifetime

• Once the most sensitive components (LPSI and CSS pump motors and level sensors) are in the MCSs of dominant sequences, the relative contribution of the sequences to the total risk of Large LOCA remains approximately the same with age

• The same picture can be seen for contributions to the risk associated with the different reactor states and location of the pipe break

LLOCA CDF

6.58E-089.48E-08

1.53E-07

6.19E-07

7.18E-08

0.E+00

1.E-07

2.E-07

3.E-07

4.E-07

5.E-07

6.E-07

7.E-07

10 20 30 40

Age

CD

F CDF in time

CDF ref.

Fussel-Vesely Impotance

0.00E+002.00E-024.00E-026.00E-028.00E-021.00E-011.20E-011.40E-011.60E-011.80E-01

10 20 30 40

Age

LPSI pump Fail toRun

LPSI valves Fail toOpen

HE on RWSTsensors

CSS pump Fail toRun

RWST level sensors

CSS pump's motorFail to Start

October 17, 2008 35

Plant Level Effect

LLOCA CDF

1.92E-07

7.18E-08

6.19E-07

0.E+00

1.E-07

2.E-07

3.E-07

4.E-07

5.E-07

6.E-07

7.E-07

10 20 30 40

Age

CD

F

CDF in time (sensitivity)

CDF ref.

CDF in time (base case)

• Sensitivity analysis of the reliability model chosen for the pump motors

• As a base case, the log-linear model was considered. The Weibull model was applied as alternative for sensitivity analysis

• The generic conclusion from this analysis is the need to examine the accuracy of several model alternatives before applying one to PSA

Relative increasing of failure probability

0.10

1.00

10.00

100.00

1000.00

10000.00

10 20 30 40

Age

CSS pump's motors(log linear)

pump's motors(Weibull)

October 17, 2008 36





5. CONCLUSIONS

October 17, 2008 37

Conclusions

Ageing effects can alter the conclusions of reference PSA studies. In particular, they can impact on:

• system unavailability and CDF,• dominant accident sequences and contributors to CDF,• component risk importance measures.

Considering ageing effects in PSA and reliability analysis could help in the selection and prioritization of SSCs for ageing management and maintenance optimization as part of a risk-informed decision-making process

The main problems relate to the methodology, data and resource availability

The purpose of the EC-JRC Ageing PSA Network’s activities is to provide PSA engineers with practical approaches, methods and advice on how evaluate the importance of ageing issues by means of PSA modeling. The results presented demonstrate methods and approaches proposed for the selection of SSCs susceptible to ageing, the development of time-dependent reliability models and the evaluation of ageing effects on overall plant safety

October 17, 2008 38

Summary of 2008 activities

• Scientific publications : • 1 published + 2 submitted (Task 4)

• Case Studies : 2 completed • Task 3 EUR report is ready for registration• Task 4 EUR 23079 EN is published

• End users support : • Guideline for Analysis of Data Related to Aging of NPP Components and Systems –

final version is ready for the review (JRC reference report, Task 4)• Collaboration in ISTC project on APSA for Armenian NPP

• Policy Support : • E&I Training on Advanced Time-Dependent Reliability Data Analysis, 6-9 October

2008• Contribution as Invited expert to IAEA workshop on VVER PSA

• Results dissemination : • PSA’2008 Int. Conference – 6 papers presented by Network participants • interview for EUROSAFE Tribune• Update of APSA Web-site

October 17, 2008 39

Conclusions

The following topics are planned for further development :

areas of possible PSA application in ageing management via risk-informed approaches (Task 8),

reliability data collection and parameter estimation : CCF (Task 5),

passive component age-dependent reliability models (Task 6),

the incorporation of ageing effects into the PSA model (Task 7).

Network Meeting December 4-5, 2008, Prague, Czech Republic.

October 17, 2008 40

Thank you very much for your attention!

Documents

"PSA AS A TOOL FOR EVALUATION OF AGEING EFFECTS ON THE SAFETY OF NPPs" Andrei Rodionov