reporting on i&c status & recommendations to the iaea on npp i&c

Reporting on I&C Status&

Recommendations to the IAEA on NPP I&C

IAEA TWG-NPPIC meeting, Vienna, May 20-22 2009

Dr. Davide Roverso

Manager COSS

OECD Halden Reactor Project

Institute for energy technology (IFE)

NORWAY

2

Nuclear installations in Norway

• The Institute for energy technology, , operates two research reactors, the only nuclear installations in Norway

• Halden Boiling Water Reactor (HBWR)• 20 MW, used for research on fuel and materials• High burn-up, water chemistry, stress corrosion cracking, ...

• JEEP II Reactor – Kjeller• 2 MW, used for basic physics research,• Neutron source for Neutron Activation Analysis (NAA)• Nanomaterials, silisium doping, ...

3

NPP I&C Activities • Most NPP I&C activities at IFE are conducted as part of

the OECD Halden Reactor Project (HRP)• International co-operative effort affiliated to OECD NEA in Paris• Project established in 1958 (50 years’ celebrated in 2008)• Jointly funded by its Members:

• 18 countries • > 100 nuclear organisations world wide

• Hosted and run by IFE, Norway• Participant types

• Utilities, Vendors, Licensing Authorities and R&D centres

4

HRP MembersSignatory members:• Norway – IFE

• Institutt for energiteknikk• Belgium - SCK/CEN

• Belgian Nuclear Research Centre• Denmark - Risø DTU

• Risø National Laboratory • Finland - Finnish Ministry of Trade and

Industry• Operator VTT

• France - EDF• Electricité de France

• Germany - GRS• Gesellschaft für Anlagen- und Reaktorsicherheit• BMFT, Utilities (VGB), Siemens (AREVA)

• Japan - JAEA• Japan Atomic Energy Agency

• Korea - KAERI• Korean Atomic Energy Research Institute

• Spain - CIEMAT• Spanish Centro de Investigaciones Energéticas,

Medioambientales y Tecnológicas• Sweden – SSM

• SSM (SKI), Swedish Radiation Safety Authority• Utilities, Westinghouse Atom

• Switzerland – HSK• Swiss Federal Nuclear Safety Inspectorate

• UK - Nexia Solutions (BNFL)• USA - USNRC

• United States Nuclear Regulatory Commission

and as Associated members:• Czech Rep. - NRI

• Czech Nuclear Res.Institute• France - IRSN

• French Institut de Radioprotection et de Sûreté Nucléaire

• Hungary - KFKI • Atomic Energy Res. Inst.

• Kazakhstan – Ulba Metallurgical Plant• Russia - “TVEL” Company

• Russian Research Centre “Kurchatov”• Slovakia - VUJE

• Nuclear Power Plant Research Institute• USA

• Westinghouse, EPRI and GE• Japan

• CRIEPI, Mitsubishi and 11 utilities

5

HRP Activity Sectors

• Nuclear Safety and Reliability• Operation of Halden BWR• Fuel and Materials technology research• 140 employees

• Safety MTO – Man Technology and Organization• Human performance and reliability• Control room technology• Virtual Reality (VR) technology• Operator Support Systems• Software Systems Dependability• 85 employees

I&C

6

HAMMLAB Experimental Facility

7

Human Performance/Human Reliability

0

1

2

3

4

5

6

7

4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

Rea

cto

r L

evel

Crew A Crew B

0

0.51

1.52

2.53

3.54

4.5

4 7

10 13 16 19 22 25

Rea

ctor

Lev

el

LOCA variant 1 LOCA variant 2

Task 1 - Response Time Isolation of Leakage

Hou

rs :

Min

utes

: S

econ

ds

crew: A crew: B crew: C crew: D crew: E crew: F crew: G

1 2 3 40:00:00

0:02:00

0:04:00

0:06:00

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0:10:00

0:12:00

0:14:00

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0:18:00

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Task 2 - reponse time isolation of leakage

Hou

rs:M

inut

es:S

econ

ds

crew: A crew: B crew: C crew: D crew: E crew: F crew: G

1 2 3 40:00:00

0:02:00

0:04:00

0:06:00

0:08:00

0:10:00

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C o lle ctive -E ffica cy Es tim a te s Acro s s th e Tw e lve Sce n a rio R u n sR1 ; L S M e a n s

Cu rre n t e ffe ct: F(1 1 , 1 8 7 )=4 ,5 4 0 6 , p = ,0 0 0 0 0

E ffe cti ve h yp o th e si s d e co m p o si ti o n

V e rti ca l b a rs d e n o te 0 ,9 5 co n fi d e n ce i n te rva l s

RUN-1RUN-2

RUN-3RUN-4

RUN-5RUN-6

RUN-7RUN-8

RUN-9RUN-1 0

RUN-1 1RUN-1 2

4 ,6

4 ,8

5 ,0

5 ,2

5 ,4

5 ,6

5 ,8

6 ,0

6 ,2

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6 ,6

Co

llective

Effica

cy Sco

re

Expert Judgement o f T eamwork Q ua lity Across the T we lve Scenario RunsR 1 ; L S Me a n s

C u rre n t e ffe ct: F(1 1 , 5 5 )=2 ,8 2 9 8 , p =,0 0 5 3 3

E ffe ctive h yp o th e s is d e co m p o s itio n

Ve rtica l b a rs d e n o te 0 ,9 5 co n fid e n ce in te rva ls

1 -R U N2 -R U N

3 -R U N4 -R U N

5 -R U N6 -R U N

7 -R U N8 -R U N

9 -R U N1 0 -R U N

1 1 -R U N1 2 -R U N

3 ,0

3 ,5

4 ,0

4 ,5

5 ,0

5 ,5

6 ,0

6 ,5

7 ,0

7 ,5

Te

am

Ba

rs Sco

re

First scenario Last scenario

Exploratory StudyHome

Plant Training

Field visits

8

Innovative Human System Interfaces

Task based displays Function oriented displays Ecological displays

Innovative BWR displays

9

Design of Large Screen Displays

10

Mixed Reality for Design, Planning & Training

11

SW system dependability

System A System CSystem B

Requirements

Design

Implementation

Development life cycle

Similar characteristics of different development phases of different systems

12

Computerised Operation Support

Performance Monitoring

Computerised Procedures

Advanced Alarm Systems

Condition Monitoring

Knowledge Management

Work Processes

Simulator technology

Function Allocation

Core Monitoring and Simulation

Prognostics

Virtual Sensing

13

Highlights

• Large-scale Signal Validation• Vision-based Diagnostics• Cable Monitoring• Mímir Framework & Toolbox• Prognostics

• Recommendations to the IAEA TWG-NPPIC• HOLMUG 2009

14

Large-scale Signal Validation

• Increase the applicability of signal validation and diagnostic tools

• Method needed for supporting monitoring of a large number of signals

• Signal grouping + Ensemble of models• Each model handles a small group of signals

Mario Hoffmann, Giulio Gola

15

The multi-group ensemble approach

Hundreds of

signals

Single validation

model

Validated signals

20-60 signals

Single validation

model

Validated signals

? ?

16

The multi-group ensemble approach

Hundreds of signals

Single validation

model

Validated signals

20-60 signals

Single validation

model

Validated signals

Hundredsof signals

Multi-group ensemble approach

Group generation

Model 1

Model aggregation

Validated signals

Model 2

Model K

Group 1

Group 2

Group K12

3

17

The multi-group ensemble approach: issues

1 Group generation

2 Ensemble model

3 Ensemble aggregation

• Optimized (MOGA) • Randomized (RFSE)

• Artificial Neural Networks (PEANO) • Principal Components Analysis (PCA)

• Weighted average • Simple average• Trimmed mean, Median

18

Applications

1) 84 signals from Oskarshamn BWR

2) 215 signals from Loviisa PWR

3) 920 simulated signals Forsmark-3 BWR (HAMBO)

19

Loviisa – 215 Signals1Signal grouping: optimized; 150 groups; 8 – 147 signals

2Validation model: PCA

3Ensemble model aggregation: Weighted average

Reconstruction of signal 205 (steam temp. in condenser SD51, °C): ensemble VS single model

Ensemble

Single model

MAE 2.84 22.03

Max AE

16.77 46.41

20

Ongoing work

• Verification of the proposed procedure on 802 measured signals from Oskarshamn BWR

• Implementation of a randomized-wrapper grouping technique

• Implementation of the final grouping scheme in the PEANO signal validation system • Within 2009

Giulio Gola

21

Vision-Based Diagnostics

Compressor Heat exchanger

Mechanical Systems

Electrical Systems

Internal breaker connection problem.

Hot fuse connection.

22

• Converts gray-scale images (with linear color palette and upper/lower temperature bounds) into temperature images

• Automatic monitoring and analysis of visual/thermographic images/segments and detection of anomalies compared to previous snapshots

• Image augmentation to visualize when crossing pre-determined thresholds

• Upon anomaly detection, initiates image sequence recording

• Image sequence playback

The Vision Application

25°C 36°C

gray

ir-1

ir-2

ir-3

ir-4

ir-5

gray ir-1 ir-2

ir-3 ir-4 ir-525°C 36°C

gray

ir-1

ir-2

ir-3

ir-4

ir-5

gray ir-1 ir-2

ir-3 ir-4 ir-5

23

Tests at the Halden ReactorThermographic observation of valve heating up:

24

Tests at the Halden Reactor cont.

31

31.5

32

32.5

33

33.5

34

34.5

35

35.5

36

36.5

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55

Index 42 - Upper average intensity limit reached for segment 'Valve_02'

Index 54 - Upper relative fraction limit reached for segment 'Valve_02'

Index 57 - Upper average intensity limit reached for image

Time index

Tem

per

atur

e

25

Tests at the Halden Reactor

0

10

20

30

40

50

60

70

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Index 29 - Upper relative fraction limit reached for segment 'SteamBoxThermo'

Index 30 - Upper threshold fraction limit reached for image

Index 33 - Upper average intensity limit reached for segment 'SteamBoxThermo'

Index 35 - Upper average intensity limit reached for image

Time index

Tem

per

atur

e

26

Cable Monitoring

• LIRA (LIne Resonance Analysis) as a Cable Analyzer

• Local degradation detection and localization• Thermal degradation• Mechanical damage• Gamma irradiation damage

• Global degradation assessment and residual life estimation• Thermal degradation• Gamma irradiation degradation• Harsh environment

Paolo Fantoni

27

Local degradation detection with LIRA

• Based on discontinuities of the characteristic impedance caused by mechanical or thermal degradation

• Sensitive to very small electric properties change (5pF/m for 0.3m in the picture)

• Localization error average less than 0.3% of total length

Hotspot at 50m ΔP

28

Comparison Tests (Tecnatom) • Compared Techniques

• Line Resonance Analysis (LIRA)• Elongation at Break (EAB)• Time-Domain Reflectometry (TDR)• Insulation Resistance (IR)

TDR LIRA

29

AVGERR= 0.23 % of cable lengthSTD = 0.08%

LIRA - Localization Accuracy

Localization Error (% cable length)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Sample

ER

R(%

)

30

Global degradation assessment• 3 EPR samples, 20 m long,

were aged at 140 °C for 10, 20 and 30 days, producing a thermal degradation equivalent to 20, 40 and 60 years

• Developed and tested two measures

• CBAC: Central Band Attenuation for Capacitance

• CBAL: Central Band Attenuation for Inductance

EPR Global Ageing

40

45

50

55

60

65

70

75

TEREF TEG1 TEG2 TEG3

CB

AC

20 yearsequivalent

40 yearsequivalent

60 yearsequivalent

Newcable

31

EAB/CBAC Correlation, EPR (TECNATOM)EAB/CBAC correlation

0

100

200

300

400

500

600

700

800

900

1000

50 55 60 65 70 75

CABC

EA

B (

%)

20 years

60 years

CBAC

40 years

new

LIRA Global degradation measure

Elon

gatio

n At

Bre

ak

32

Info

rmat

ion

Dat

aK

now

ledg

eIn

telli

genc

e

Data Validation,Reconstruction, and

Calibration Monitoring

Early Fault Detection

and Diagnostics

Lifetime and Performance

Prediction...

. . .DataConditioning

DataFiltering

Feature Extraction

DataNormalization

Input Selection

Data Clustering

Statistical Analysis

Modeling

Pattern Classification

RegressionEstimation

UncertaintyEstimation

DataInput

HypothesisTesting

Performance Analysis

RiskOptimization

TOOLBOX

External Tools(e.g. SAS)

DataConditioning

DataConditioning

DataFiltering

DataFiltering

Feature Extraction

Feature Extraction

DataNormalization

DataNormalization

Input Selection

Input Selection

Data Clustering

Data Clustering

Statistical AnalysisStatistical Analysis

ModellingModeling



RegressionEstimationRegressionEstimation

UncertaintyEstimationUncertaintyEstimation

HypothesisTesting

HypothesisTesting



RiskOptimisation

RiskOptimization



DataInputDataInput

Mímir

33

Why Mímir

aladdin

Performs early fault detection and diagnosis through the dynamic recognition of observable changes in measurement signals

aladdin

Performs early fault detection and diagnosis through the dynamic recognition of observable changes in measurement signals

PEANO

A system for Signal Validation and On-line Calibration Monitoring based on auto-associative empirical models

PEANO

A system for Signal Validation and On-line Calibration Monitoring based on auto-associative empirical models

Virtual Sensing

Empirical Ensemble-Based Virtual Sensing using regression models to estimate quantities not directly measured with physical instruments

Virtual Sensing

Empirical Ensemble-Based Virtual Sensing using regression models to estimate quantities not directly measured with physical instruments

Signal Grouping

Signal grouping for large scale applications through the use of Random Feature Selection Ensemble

Signal Grouping

Signal grouping for large scale applications through the use of Random Feature Selection Ensemble

34

Why Mímir

Filtering

Feature Selection

Clustering

Normalisation

Neural Network

Classification

Regression Model

Filtering

Neural Network

Filtering

Normalisation Ensembles

Regression Model

Normalisation

Genetic AlgorithmsEnsembles

Ensembles

Mìmir

Wavelet Filtering

Feature Selection

Clustering

Filtering

Normalisation Neural Network

ClassificationRegression Model

Ensembles

…

……

…

… …

Genetic Algorithms

35

Early Fault Detection

and Diagnostics



Info

rmat

ion

Dat

aK

now

ledg

eIn

telli

genc

e



Lifetime and Performance

Prediction…


Feature Extraction

Input Selection

Data Clustering


Modelling



HypothesisTesting


RiskOptimisation

DataConditioning

Feature Extraction

Input Selection


Modelling



HypothesisTesting


RiskOptimisation

DataConditioning

Feature Extraction

Input Selection


Modelling



HypothesisTesting


RiskOptimisation

TOOLBOX




Data Clustering

Data Clustering

Data Clustering

RegressionEstimationRegressionEstimationRegressionEstimationRegressionEstimation

DataFiltering

DataFiltering

DataFiltering

DataFiltering

DataNormalization

DataNormalization

DataNormalization

DataNormalization

DataInputDataInputDataInputDataInput

Mímir

36

Industry Standards

• ISO-13374• Condition monitoring and diagnostics

of machines – Data processing, communication and presentation

• MIMOSA OSA-CBM• Open System Architecture for

Condition-based Maintenance (OSA-CBM)

• Implementation of ISO-13374• A standard architecture for moving

information in a condition-based maintenance system

• Mìmir • Is being designed to be compliant

with ISO-13374 and the MIMOSA OSA-CBM specification

37

ISO-13374 and Mimosa’s OSA-CBM

Data Acquisition (DA)

Data Manipulation (DM)

State Detection (SD)

Health Assessment (HA)

Prognostics Assessment (PA)

Advisory Generation (AG)External systems,

data archiving,and block

configuration

Technicaldisplays andinformation

presentation

Sensor / Transducer / Manual entry

38

Mímir Demonstrator

• Version 1• Based on Java Plug-in Framework

• October 2008

• Version 2• Based on OSA-CBM Modular Implementation Framework

• Penn State University, Applied Research Lab (ARL)• U.S. Army Logistics Innovation Agency (USALIA)

Add new modules by simply dropping in “zip” files in the plugins folderAdd new modules by simply dropping in “zip” files in the plugins folder

39

Example Case Study Signal Validation of 14 Signals from Oskarshamn O-3

40

Example Case Study Signal Validation of 14 Signals from Oskarshamn O-3

Nr.

Label Tag Sub-system Measurement Unit

Range

Min Max

1 7 260KW316 Fuel comp. Calc. Flow kg/s 0 200

2 9 260KW951 Fuel comp. Calc. Power MW 0 4000

3 11 312KA502 Feed Water Lines Temperature °C 0 250

4 13 312KC502 Feed Water Lines Temperature °C 0 250

5 34 422KA111 Steam Reheating Pressure MPa 0 1

6 43 423KB501 Steam Extraction Temperature °C 0 300

7 45 423KB503 Steam Extraction Temperature °C 0 300

8 50 441KB509 Main Cooling Water Temperature °C 0 60

9 55 462KA109 Condensate Pressure MPa 0 4

10 58 462KA503 Condensate Temperature °C 0 60

11 73 463KA503 Turbine plant feedwater Temperature °C 0 200

12 76 463KB501 Turbine plant feedwater Temperature °C 0 300



41

Signal Validation in a Nutshell

PlantSignals

SignalValidation

ValidatedSignals

Plant Signals

EstimateSignals

ValidatedSignals

ResidualCalculation

SignalHealth

Assessment

42

...

Info

rmat

ion

Dat

aK

now

ledg

eIn

telli

genc

e


DataFiltering

Feature Extraction

DataNormalization

Input Selection

Data Clustering


Modeling


RegressionEstimation


DataInput

HypothesisTesting


RiskOptimization

TOOLBOX


DataConditioning

DataConditioning

DataFiltering

DataFiltering

Feature Extraction

Feature Extraction

STDNormalization

DataNormalization

Input Selection

Input Selection

Data Clustering

Data Clustering

Statistical AnalysisStatistical Analysis

ModellingModeling



PCAEstimationRegressionEstimation

UncertaintyEstimationUncertaintyEstimation

SPRTHypothesisTesting



RiskOptimisation

RiskOptimization



DataInputDataInput

Mímir

43

Test_1 Test_2 Test_3

I/O Display

NormaliseSTD

Signal Validation in Mímir - Simple Set-up



State Detection (SD)

Health Assessment (HA)

Prognostics Assessment (PA)

Advisory Generation (AG)

SPRT

σ-2σ FixedBound

PCA

AANN

I/O Data Feeder

DenormaliseSTD

C# Matlab

Fortran Java

DataDisplay

TrendGraph

45

Case Tests

• Test 1 – Signal Offset• PCA Reconstruction• AANN Reconstruction• PEANO Reconstruction• σ-2σ Fixed Bounds Signal Health Assessment (on PCA

residual)• SPRT Signal Health Assessment (on PCA residual)

• Test 2 - Signal drift• PCA Reconstruction• AANN Reconstruction• PEANO Reconstruction• σ-2σ Fixed Bounds Signal Health Assessment (on PCA

residual)• SPRT Signal Health Assessment (on PCA residual)

46

Test_1 Test_2 Test_3

NormaliseSTD

Signal Validation in Mímir – Simple Set-up



Health Assessment (HA)SPRT

σ-2σ FixedBound

PCA

AANN

I/O Data Feeder

DenormaliseSTD

C# Matlab

Fortran Java

I/O Display

DataDisplay

TrendGraph

47

0 5000 10000 15000-1000

0

1000

2000

3000

4000Thermal Power (MWt) - Offset 20%

Real value

Input to PCAPCA reconstruction

0 5000 10000 15000-1500

-1000

-500

0

500

1000Mismatch between input signal and PCA estimate

PCA reconstruction of signal offset

0 5000 10000 15000-1000

0

1000

2000

3000


Real value


0 5000 10000 15000-1500

-1000

-500

0

500


48

0 5000 10000 15000-1000

0

1000

2000

3000


Real value

Input to AANN

AANN reconstruction

0 5000 10000 15000-1500

-1000

-500

0

500

1000Mismatch between input signal and AANN estimate

AANN reconstruction of signal offset

0 5000 10000 15000-1000

0

1000

2000

3000


Real value

Input to AANN

AANN reconstruction

0 5000 10000 15000-1500

-1000

-500

0

500


49

PEANO reconstruction of signal offset

0 5000 10000 15000-1000

0

1000

2000

3000

4000Termal Power (MWt) - Offset 20%

Real value

Input to PEANOPEANO reconstruction

0 5000 10000 15000-1500

-1000

-500

0

500

1000Mismatch between input signal and PEANO estimate

0 5000 10000 15000-1000

0

1000

2000

3000

4000Termal Power (MWt) - Offset 20%

Real value

Input to PEANOPEANO reconstruction

0 5000 10000 15000-1500

-1000

-500

0

500


50

0 5000 10000 15000-1000

0

1000

2000

3000


Real value


0 5000 10000 15000

Faulty

Warning

Healthy

Thermal Power (MWt) - Offset 20%

SPRT health assessment based on PCA residual

51

0 5000 10000 15000100

150

200

250Feed Water Line Temperature (C) - Drift 10%

Real value


0 5000 10000 15000-20

-10

0

10

20


PCA reconstruction of signal drift

0 5000 10000 15000100

150

200


Real value


0 5000 10000 15000-20

-10

0

10

20


52

0 5000 10000 15000100

150

200


Real value

Input to AANNAANN reconstruction

0 5000 10000 15000-150

-100

-50

0


AANN reconstruction of signal drift

0 5000 10000 15000100

150

200


Real value

Input to AANNAANN reconstruction

0 5000 10000 15000-150

-100

-50

0


53

PEANO reconstruction of signal drift

0 5000 10000 15000100

150

200

250Feedwater Line Temperature (C) - Drift 10%

Real value

Input to PEANO

PEANO reconstruction

0 5000 10000 15000-30

-20

-10

0

10

20


0 5000 10000 15000100

150

200

250Feedwater Line Temperature (C) - Drift 10%

Real value

Input to PEANO

PEANO reconstruction

0 5000 10000 15000-30

-20

-10

0

10

20


54

0 5000 10000 15000100

150

200


Real value


0 5000 10000 15000

Faulty

Warning

Healthy

Feed Water Line Temperature (C) - Drift 10%

SPRT health assessment based on PCA residual

55

A Prognostics case study from Oskarshamn O1 Use of dP measures over heat exchanger filters

56

OKG Case Study: Use of differential pressure measures for CBM of heat exchanger filters•Maintenance orders

•Differential pressure measurements

•Flow measurements

•(Pump status, generator effect)

57

Work orders

Pre-planned Based on expert judgement (condition)

All Heat Exchangers

0

1

2

3

4

5

0 100 200 300 400 500 600 700 800

Days

E1

E2

E3

E4

Dec

06

Mar

07

Jun

07

Sep

07

Dec

07

Mar

08

Jun

08

Sep

08

Dec

08

58

Differential pressure at first heat exchanger

721E1 Pressure

-0.5

0

0.5

1

1.5

2

0 100 200 300 400 500 600 700 800

K209 (1) AO FU Diff.Pressure

59

Flow measurement at first heat exchanger

721E1 Flow

0

20

40

60

80

100

120

140

160

0 100 200 300 400 500 600 700 800

K341 AO FU

60

Feature based on Bernoulli`s principle

Incompressible flow equation: .2

2

constp

gzv

Diff P/Sqare Flow

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

0.0008

0 100 200 300 400 500 600 700 800

Diff/sqare flow AO FU

Can`t be explained by p and v

• trend of status of pumps ?

• trend of generator effect (revision) ?

• trend of sea water temperature ?

• other ?

61

Recommendations to the IAEA

62

Recommendations to the IAEA (1)

• Standards• Identification and analysis of existing standards for

condition-monitoring, diagnostics and prognostics• ISO-13374• MIMOSA OSA-CBM• …

• Can these be applied as-is to the nuclear industry or does the nuclear industry need new specific standards?

63


• “Aging” of digital systems• Digital I&C and SW systems have comparatively

very short life spans due to rapid technological advances

• Systems need to have technology modernisation and replacement as a fundamental design requirement in order to age gracefully

• Possible approaches could include:• Identification of several levels of abstraction in the system design and

architecture so that lower levels close to the implementation can be more easily modernised swapping obsolete components with modern ones without affecting the overall system

• Investigate automatic code generation from platform-independent specifications

64


• Uncertainty Management• Highly automated I&C and SW systems will rely on

real time data and additional information originating from other systems (e.g. condition monitoring and diagnostic systems)

• Most sources of information will have associated a certain degree of uncertainty that will have to be appropriately assessed and taken into account in further information processing

• Mechanisms for defining and treating uncertain information will be necessary

65


• Advances in Human System Interfaces• New I&C and SW systems deployed in new

settings will require new HSI solutions• New work practices, higher automation, modular plant designs

• Context-aware, multi-abstraction, multi-user HSIs• Emerging technologies could enable new work

practices• Augmented Reality and hand-held technologies enable portable

access to technology and advanced guidance• New interaction and collaboration technologies for distributed

decision-making• User interfaces dynamically integrating data from multiple sources• Integrated Operations

66

Recommendations to the IAEA (5)• Interrelation between Technological, Human and

Organisational Factors• Factors related to structure, including established roles and

responsibilities (within a specific role), established task description procedures, established training procedures (often activity-oriented), and established supervision and management strategies

• Factors related to culture, including the manners of using artefacts to produce cultural contents, Artefacts can be of tangible nature, ones such as manuals, computers, etc., or of intangible nature, such as language, ethical values, senses of realism, etc.

• Factors related to process, that are directly the result of using cultural contents to produce cultural expressions – the manifestation of the contents. Examples here are established or “accepted” ways of communication, experienced patterns of conflicts, and experienced ways of handling changes

Atoosa P-J Thunem

Invitation

6th HOLMUG meeting(Halden On-Line Monitoring User Group)

Loviisa, Finland, October 8th- 9th, 2009

68

• Objectives• Information dissemination and User Feedback

on on-line monitoring:• Methods• Available systems• Regulatory aspects• Feedback from utilities, research institutes, universities, and vendors

• Previous Meetings• 2003 in Halden, • 2004 at the EHPG in Sandefjord• 2004 IAEA technical meeting on ”Increasing instrument

calibration interval through on-line monitoring technologies”, in Halden

• 2006 at Oskarshamn (OKG), Sweden• 2007 at Olkiluoto (TVO), Finland• 2008 at Ringhals (Vattenfall), Sweden

69

Topics• Calibration monitoring and signal validation

• Tools (e.g. PEANO) and methods• Experiences

• Equipment condition monitoring• Tools (e.g. aladdin, TEMPO, LIRA) and methods• Experiences

• Core Surveillance• Tools (e.g. SCORPIO, VNEM) and methods• Experiences

• Regulatory aspects• Requirements and experiences

You are welcome and encouraged to contribute!

70

Meeting location

The meeting will be held at the Loviisa NPP site on the Southern coast of Finland

71

Meeting information

• Expression of interest: June 30th, 2009• Contribution deadline: September 18th, 2009• Registration deadline: September 23rd, 2009

• Accommodation: Sannäs Manor

www.sannaskartano.fi• Visits: Loviisa plant site-tour

Finnish Sauna

http://www.ife.no/events/holmug2009

reporting on i&c status & recommendations to the iaea on npp i&c

Documents

research reactors

basic physics research

npp ic activities

nri czech nuclear res

reactor kjeller2 mw

associated members

ic status recommendations

neutron source