The main objective of generation risk assessment (GRA) is to assess the impact of equipment unavailability and failures on the ability of the plant to produce power over time. The system reliability models employed for this purpose are based on the standard fault tree/event tree approach, which assumes failure rates to be constant. However, this ignores the impact of aging degradation and results in static estimates of expected generation loss. Component and equipment degradation not only increases the probability of failure over time, but also contributes to generation risk through increased unavailability and costs arising from greater requirement for inspection and replacement of degraded components. This paper discusses some of the key challenges associated with integrating the results of component degradation models into GRA. Because many analytical and simulation methods are subject to limitations, the methodology and modeling approach proposed in this work builds on the current GRA practice using the fault tree approach. The modeling of component degradation can be done separately at the fault tree cut set level, assuming the cut sets are independent and the component unavailabilities are relatively small. In order to capture the joint contribution of equipment failure and unavailability to generation risk, new risk-based importance measures are also developed using the concept of net present value.

1.
Electric Power Research Institute (EPRI)
, 2004, “
Introduction to Simplified Generation Risk Assessment Modeling
,” Report No. 1007386, EPRI, Palo Alto, CA.
2.
Electric Power Research Institute (EPRI)
, 2004, “
Generation Risk Assessment (GRA) Plant Implementation Guide
,” Report No. 1008121, EPRI, Palo Alto, CA.
3.
Electric Power Research Institute (EPRI)
, 2005, “
Generation Risk Assessment (GRA) at Cooper Nuclear Station
,” Report No. 1011924, EPRI, Palo Alto, CA.
4.
Electric Power Research Institute (EPRI)
, 2008, “
Case Study: A Comparison of Generation Risk Assessment and Failure Modes and Effects Analysis Methodologies at TEPCO’s Nuclear Power Plants
,” Report No. 1016461, EPRI, Palo Alto, CA and TEPCO, Tokyo, Japan.
5.
Electric Power Research Institute (EPRI)
, 2002, “
Risk-Informed Asset Management (RIAM) Development Plan
,” Report No. 1006268, EPRI, Palo Alto, CA.
6.
Electric Power Research Institute (EPRI)
, 2005, “
Risk-Informed Asset Management (RIAM)—Method, Process, and Business Requirements
,” Report No. 1009632, EPRI, Palo Alto, CA.
7.
Smith
,
C. L.
,
Shah
,
V. N.
,
Kao
,
T.
, and
Apostolakis
,
G.
, 2001, “
Incorporating Aging Effects Into Probabilistic Risk Assessment—A Feasibility Study Utilizing Reliability Physics Models
,” Report No. NUREG/CR-5632, U.S. NRC, Washington, DC.
8.
Nuclear Regulatory Commission (NRC)
, 2001, “
Generic Aging Lessons Learned (GALL) Report
,” Report No. NUREG-1801, U.S. NRC, Washington, DC.
9.
McCormick
,
N. J.
, 1981,
Reliability and Risk Analysis: Methods and Nuclear Power Applications
,
Academic
,
New York
.
10.
Kee
,
E.
,
Sun
,
A.
,
Richards
,
A.
,
Liming
,
J.
,
Salter
,
J.
, and
Grantom
,
R.
, 2004, “
Using Risk-Informed Asset Management for Feedwater System Preventive Maintenance Optimization
,”
J. Nucl. Sci. Technol.
0022-3131,
41
(
3
), pp.
347
353
.
11.
Electric Power Research Institute (EPRI)
, 2002, “
Reliability and Preventive Maintenance: Balancing Risk and Reliability—For Maintenance and Reliability Professionals at Nuclear Power Plants
,” Report No. 1002936, EPRI, Palo Alto, CA.
12.
Rausand
,
M.
, and
Hoyland
,
A.
, 2004,
System Reliability Theory: Models, Statistical Methods, and Applications
,
2nd ed.
,
Wiley
,
Hoboken, NJ
.
13.
Ascher
,
H.
, and
Feingold
,
H.
, 1984,
Repairable System Reliability: Modeling, Inference, Misconceptions and Their Causes
,
Dekker
,
New York
.
14.
Birolini
,
A.
, 2007,
Reliability Engineering: Theory and Practice
,
5th ed.
,
Springer-Verlag
,
Berlin
.
15.
Epstein
,
B.
, and
Weissman
,
I.
, 2008,
Mathematical Models for Systems Reliability
,
Chapman and Hall
,
London
/
CRC
,
Boca Raton, FL
.
16.
Hwang
,
C. L.
,
Tillman
,
F. A.
, and
Lee
,
M. H.
, 1981, “
System Reliability Evaluation Techniques for Complex/Large Systems. A Review
,”
IEEE Trans. Reliab.
0018-9529,
R-30
, pp.
416
423
.
17.
Kovalenko
,
I. N.
,
Kuznetsov
,
N. Y.
, and
Pegg
,
P. A.
, 1997,
Mathematical Theory of Reliability of Time Dependent Systems With Practical Applications
,
Wiley
,
Chichester, UK
.
18.
Ridgon
,
S. E.
, and
Basu
,
A. P.
, 2000,
Statistical Methods for the Reliability of Repairable Systems
,
Wiley
,
New York
.
19.
Wang
,
H.
, and
Pham
,
H.
, 2006,
Reliability and Optimal Maintenance
,
Springer-Verlag
,
London
.
20.
Vesely
,
W. E.
,
Godberg
,
F. F.
,
Roberts
,
N. H.
, and
Haasl
,
D. F.
, 1981, “
Fault Tree Handbook
,” Report No. NUREG-0492, U.S. NRC, Washington, DC.
21.
Fabrycky
,
W. J.
, and
Blanchard
,
B. S.
, 1991,
Life-Cycle Cost and Economic Analysis
,
Prentice-Hall
,
Englewood Cliffs, NJ
.
22.
Datla
,
S. V.
,
Jyrkama
,
M. I.
, and
Pandey
,
M. D.
, 2008, “
Probabilistic Modelling of Steam Generator Tube Pitting Corrosion
,”
Nucl. Eng. Des.
0029-5493,
238
(
7
), pp.
1771
1778
.
You do not currently have access to this content.