Engineering & Projects Organization. Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.1

Transcription

1 Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.1

2 Finding the best approach for I&C modeling in the PSA H. Brunelière AREVA NP SAS PSA team Vienne, 2 nd October 2012

3 Agenda Introduction Compact model Detailed modeling Elaboration and documentation of the methodology Implementation of the I&C signals in the PSA Advantages Conclusion Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.3

4 Introduction Methodology for modeling of probabilities of failure per demand of I&C functions in the PSAs of Nuclear Power Plants Applicable to new and existing plants I&C reliability analyses (fault trees for modeling I&C in a dedicated model out of the PSA) are not in the scope of this paper Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.4

5 Introduction Need for a modeling consistent with design progress From a compact model to a more detailed modeling Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.5

6 Introduction Instrumentation part I&C level 0 Processing part I&C level 1 Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.6

7 Compact model Each I&C signal has three parts Instrumentation part specific processing part non-specific processing part Values for unavailability Dépend of classification and architecture of systems Instrumentation Processing (Specific) Processing (non specific) Actuator Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.7

8 Instrumentation Compact model Equipment class E1A E1B, E2 or NC Redundancy 2/4 2/3 2/4, 2/3 or 1/2 1/1 Unavailability 1E-04 / demand 3E-04 / demand 1E-03 / demand 1E-02 / demand Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.8

9 Compact model Specific processing part (specific to a signal) Equipment class E1A E1B, E2, NC Unavailability 1E-04 / demand 1E-03 / demand Non-specific processing part (not specific to a signal but specific to a platform) Equipment class E1A E1B, E2, NC Unavailability 1E-05 / demand 1E-04 / demand Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.9

10 Compact model Advantages of this modeling Accounts of main contributors = common cause failures Possibility of implementation at the beginning of projects Easy to implement for sensitivity studies on design choices Easy to read the cutsets involving I&C failures Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.10

11 Detailed modeling Elaboration and documentation of the methodology Implementation of the I&C signals in the PSA Conclusion Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.11

12 Elaboration and documentation of the methodology Methodology based on Comparison performed at the beginning of 2011 between I&C models in EPR PSAs Expert and engineering judgments based on Olkiluoto 3, Taishan 1&2 and US EPR PSA models Detailed I&C reliability studies Knowledge of the systems behavior Work between AREVA NP PSA teams (SAS, Inc and GmbH) during dedicated meetings 9 people involved (including EPR I&C PSA practitioners) All aspects of the comparison addressed Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.12

13 Elaboration and documentation of the methodology Methodology document written in parallel Results of the work. Bases for modeling assumptions and reliability data substantiation. Details for practical implementation Examples included Modeling recommendations. Methodology document can be easily used in the frame of a project as part of PSA documentation. Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.13

14 Implementation of the I&C signals in the PSA Instrumentation part SENSOR Pre-Processing (optional) Sensor Conditioning Signal Multiplication and distribution I&C system 1 I&C system 2... I&C system n I&C System Link Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.14

15 Implementation of the I&C signals in the PSA Instrumentation part modeling Modeling at sensor level Conditioning modules included Failure of the instrumentation part OR Failure of the sensor XXX Failure of the conditioning module YYY XXX XXX_COND Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.15

16 Implementation of the I&C signals in the PSA Instrumentation part modeling Redundant sensors are required for the elaboration of the signal => logic gate in order to represent failure criterion Degradation of voting logics conservatively not considered in the PSA modeling Avoid complexity Conservatism negligible Addressed in detailed I&C reliability studies if necessary Voting logic Number of sensors Logic gate used in the PSA (failure criterion) 2/4 4 sensors 3 2/3 3 sensors 2 2/2 2 sensors OR 1/2 2 sensors AND 1/1 1 sensor OR Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.16

17 Implementation of the I&C signals in the PSA Instrumentation part Reliability data based on Hourly failure rate of sensors Efficiency of the internal self-tests Time interval between periodic tests Mean Time to Repair (MTTR) the component Common cause failures applied For sensors For conditioning modules when relevant Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.17

18 Implementation of the I&C signals in the PSA Processing part Elementary components used for the modeling of I&C processing parts are the single processing units Based on analyses of I&C systems, specific reliability values are determined for each unit Unit composed of Sub racks for mounting the modules Processing module(s) I/O modules Communication modules Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.18

19 Implementation of the I&C signals in the PSA Processing part Failure of SIS actuation train 1 on low delta P sat SIS_DPSAT_TRAIN1 failure of acquisition part (APU + sensors) Failure of ALU division 1 processing part Failure of non-specific processing Failure of the w hole TXS platform ALUB1_DIV1 ALUB2_DIV1 CCF_TXS failure acquisition division failure of acquisition division 2 Failure of acquisition division 3 failure of acquisition division 4 SIS_DPSAT_AC_TR1_1 SIS_DPSAT_AC_TR1_2 SIS_DPSAT_AC_TR1_3 SIS_DPSAT_AC_TR1_4 Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.19

20 Implementation of the I&C signals in the PSA Processing part Reliability data based on Inputs from manufacturer - Failure rates for each failure mode Periodic tests frequency Mean Time to Repair CCF considered Between units that perform redundant processing in the same system (application level) At platform level (value given for the loss of a whole I&C platform due to hardware or software CCF) Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.20

21 Implementation of the I&C signals in the PSA Methodology for modeling of I&C functions in the PSAs of NPPs for which the allocation of functions in the units is defined Principle of the method (except need for modeling software failures) remains applicable for not digital platforms Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.21

22 Advantages Links between I&C and support systems are easy to implement in the model Hazards analyses integrate I&C Detailed modeling of units allows the detection of asymmetries or imbalances in the I&C design (non adequate allocation of signals in the processing units) Modeling easily understandable with respect to the PSA cutsets analysis I&C architecture accurately represented in the PSA Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.22

23 Conclusion Two types of modeling Graduation of the level of detail Adequacy to progress of the project May have to be supplemented by I&C reliability analyses (out of the PSA) for justification of assumptions Finding the best approach for I&C modeling in the PSA H. Brunelière 2 nd October p.23