Probabilistic risk assessment of station blackouts in nuclear power plants

Hindolo George-Williams, Min Lee, Edoardo Patelli

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Adequate ac power is required for decay heat removal in nuclear power plants. Station blackout (SBO) accidents, therefore, are a very critical phenomenon to their safety. Though designed to cope with these incidents, nuclear power plants can only do so for a limited time, without risking core damage and possible catastrophe. Their impact on a plant's safety are determined by their frequency and duration, which quantities, currently, are computed via a static fault tree analysis that deteriorates in applicability with increasing system size and complexity. This paper proposes a novel alternative framework based on a hybrid of Monte Carlo methods, multistate modeling, and network theory. The intuitive framework, which is applicable to a variety of SBOs problems, can provide a complete insight into their risks. Most importantly, its underlying modeling principles are generic, and, therefore, applicable to non-nuclear system reliability problems, as well. When applied to the Maanshan nuclear power plant in Taiwan, the results validate the framework as a rational decision-support tool in the mitigation and prevention of SBOs.
LanguageEnglish
Pages494-512
Number of pages19
JournalIEEE Transactions on Reliability
Volume67
Issue number2
DOIs
Publication statusPublished - 1 Jun 2018

Fingerprint

Nuclear Power Plant
Risk Assessment
Risk assessment
Nuclear power plants
Safety
Fault Tree Analysis
Fault tree analysis
Critical Phenomena
Multi-state
Catastrophe
Circuit theory
System Reliability
Taiwan
Decision Support
Modeling
Accidents
Monte Carlo method
Intuitive
Monte Carlo methods
Damage

Keywords

  • accident recovery
  • monte carlo simulation (MCS)
  • nuclear power plant
  • risk assessment
  • blackout (SBO)

Cite this

@article{22c1a8d5eaeb446dbd8b5ce7cf597fc5,
title = "Probabilistic risk assessment of station blackouts in nuclear power plants",
abstract = "Adequate ac power is required for decay heat removal in nuclear power plants. Station blackout (SBO) accidents, therefore, are a very critical phenomenon to their safety. Though designed to cope with these incidents, nuclear power plants can only do so for a limited time, without risking core damage and possible catastrophe. Their impact on a plant's safety are determined by their frequency and duration, which quantities, currently, are computed via a static fault tree analysis that deteriorates in applicability with increasing system size and complexity. This paper proposes a novel alternative framework based on a hybrid of Monte Carlo methods, multistate modeling, and network theory. The intuitive framework, which is applicable to a variety of SBOs problems, can provide a complete insight into their risks. Most importantly, its underlying modeling principles are generic, and, therefore, applicable to non-nuclear system reliability problems, as well. When applied to the Maanshan nuclear power plant in Taiwan, the results validate the framework as a rational decision-support tool in the mitigation and prevention of SBOs.",
keywords = "accident recovery, monte carlo simulation (MCS), nuclear power plant, risk assessment, blackout (SBO)",
author = "Hindolo George-Williams and Min Lee and Edoardo Patelli",
note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2018",
month = "6",
day = "1",
doi = "10.1109/TR.2018.2824620",
language = "English",
volume = "67",
pages = "494--512",
journal = "IEEE Transactions on Reliability",
issn = "0018-9529",
number = "2",

}

Probabilistic risk assessment of station blackouts in nuclear power plants. / George-Williams, Hindolo; Lee, Min; Patelli, Edoardo.

In: IEEE Transactions on Reliability, Vol. 67, No. 2, 01.06.2018, p. 494-512.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Probabilistic risk assessment of station blackouts in nuclear power plants

AU - George-Williams, Hindolo

AU - Lee, Min

AU - Patelli, Edoardo

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/6/1

Y1 - 2018/6/1

N2 - Adequate ac power is required for decay heat removal in nuclear power plants. Station blackout (SBO) accidents, therefore, are a very critical phenomenon to their safety. Though designed to cope with these incidents, nuclear power plants can only do so for a limited time, without risking core damage and possible catastrophe. Their impact on a plant's safety are determined by their frequency and duration, which quantities, currently, are computed via a static fault tree analysis that deteriorates in applicability with increasing system size and complexity. This paper proposes a novel alternative framework based on a hybrid of Monte Carlo methods, multistate modeling, and network theory. The intuitive framework, which is applicable to a variety of SBOs problems, can provide a complete insight into their risks. Most importantly, its underlying modeling principles are generic, and, therefore, applicable to non-nuclear system reliability problems, as well. When applied to the Maanshan nuclear power plant in Taiwan, the results validate the framework as a rational decision-support tool in the mitigation and prevention of SBOs.

AB - Adequate ac power is required for decay heat removal in nuclear power plants. Station blackout (SBO) accidents, therefore, are a very critical phenomenon to their safety. Though designed to cope with these incidents, nuclear power plants can only do so for a limited time, without risking core damage and possible catastrophe. Their impact on a plant's safety are determined by their frequency and duration, which quantities, currently, are computed via a static fault tree analysis that deteriorates in applicability with increasing system size and complexity. This paper proposes a novel alternative framework based on a hybrid of Monte Carlo methods, multistate modeling, and network theory. The intuitive framework, which is applicable to a variety of SBOs problems, can provide a complete insight into their risks. Most importantly, its underlying modeling principles are generic, and, therefore, applicable to non-nuclear system reliability problems, as well. When applied to the Maanshan nuclear power plant in Taiwan, the results validate the framework as a rational decision-support tool in the mitigation and prevention of SBOs.

KW - accident recovery

KW - monte carlo simulation (MCS)

KW - nuclear power plant

KW - risk assessment

KW - blackout (SBO)

U2 - 10.1109/TR.2018.2824620

DO - 10.1109/TR.2018.2824620

M3 - Article

VL - 67

SP - 494

EP - 512

JO - IEEE Transactions on Reliability

T2 - IEEE Transactions on Reliability

JF - IEEE Transactions on Reliability

SN - 0018-9529

IS - 2

ER -