A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events

Chao Yan; Ding Tao; Zhaohong Bie; Lucarelli Giambattista Luca; Wang Xifan

doi:10.1109/PESGM.2016.7741903

A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events

Chao Yan
, Ding Tao
, Zhaohong Bie
, Lucarelli Giambattista Luca
, Wang Xifan

School of Electrical Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Variance Minimization (VM) technique is one of the most popular methods for importance sampling (IS), but it has never been successfully applied to composite (generation and transmission) system reliability evaluation due to the difficulty of solving. In this paper, Geometric Programming (GP) is firstly introduced to repeatedly solve the VM optimization model in multi-levels to adaptively obtain the optimal IS parameters used in a IS-Monte Carlo Simulation (MCS) based composite system reliability evaluation considering rare events. Then, the IEEE Reliability Test System and its modified version are used to test the proposed methodology, and the proposed method is compared with another important technique for IS of Cross-Entropy (CE) in estimation accuracy and convergence performance.

Original language	English
Title of host publication	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Publisher	IEEE Computer Society
ISBN (Electronic)	9781509041688
DOIs	https://doi.org/10.1109/PESGM.2016.7741903
State	Published - 10 Nov 2016
Event	2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States Duration: 17 Jul 2016 → 21 Jul 2016

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2016-November
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Country/Territory	United States
City	Boston
Period	17/07/16 → 21/07/16

Keywords

Geometric Programming (GP)
Importance sampling (IS)
Rare events
Reliability evaluation
Variance Minimization (VM)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PESGM.2016.7741903

Cite this

Yan, C., Tao, D., Bie, Z., Giambattista Luca, L., & Xifan, W. (2016). A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events. In 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 Article 7741903 (IEEE Power and Energy Society General Meeting; Vol. 2016-November). IEEE Computer Society. https://doi.org/10.1109/PESGM.2016.7741903

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title = "A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events",

abstract = "Variance Minimization (VM) technique is one of the most popular methods for importance sampling (IS), but it has never been successfully applied to composite (generation and transmission) system reliability evaluation due to the difficulty of solving. In this paper, Geometric Programming (GP) is firstly introduced to repeatedly solve the VM optimization model in multi-levels to adaptively obtain the optimal IS parameters used in a IS-Monte Carlo Simulation (MCS) based composite system reliability evaluation considering rare events. Then, the IEEE Reliability Test System and its modified version are used to test the proposed methodology, and the proposed method is compared with another important technique for IS of Cross-Entropy (CE) in estimation accuracy and convergence performance.",

keywords = "Geometric Programming (GP), Importance sampling (IS), Rare events, Reliability evaluation, Variance Minimization (VM)",

author = "Chao Yan and Ding Tao and Zhaohong Bie and \{Giambattista Luca\}, Lucarelli and Wang Xifan",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 ; Conference date: 17-07-2016 Through 21-07-2016",

year = "2016",

month = nov,

day = "10",

doi = "10.1109/PESGM.2016.7741903",

language = "英语",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2016 IEEE Power and Energy Society General Meeting, PESGM 2016",

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Yan, C, Tao, D , Bie, Z, Giambattista Luca, L & Xifan, W 2016, A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events. in 2016 IEEE Power and Energy Society General Meeting, PESGM 2016., 7741903, IEEE Power and Energy Society General Meeting, vol. 2016-November, IEEE Computer Society, 2016 IEEE Power and Energy Society General Meeting, PESGM 2016, Boston, United States, 17/07/16. https://doi.org/10.1109/PESGM.2016.7741903

A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events. / Yan, Chao; Tao, Ding ; Bie, Zhaohong et al.
2016 IEEE Power and Energy Society General Meeting, PESGM 2016. IEEE Computer Society, 2016. 7741903 (IEEE Power and Energy Society General Meeting; Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events

AU - Yan, Chao

AU - Tao, Ding

AU - Bie, Zhaohong

AU - Giambattista Luca, Lucarelli

AU - Xifan, Wang

PY - 2016/11/10

Y1 - 2016/11/10

N2 - Variance Minimization (VM) technique is one of the most popular methods for importance sampling (IS), but it has never been successfully applied to composite (generation and transmission) system reliability evaluation due to the difficulty of solving. In this paper, Geometric Programming (GP) is firstly introduced to repeatedly solve the VM optimization model in multi-levels to adaptively obtain the optimal IS parameters used in a IS-Monte Carlo Simulation (MCS) based composite system reliability evaluation considering rare events. Then, the IEEE Reliability Test System and its modified version are used to test the proposed methodology, and the proposed method is compared with another important technique for IS of Cross-Entropy (CE) in estimation accuracy and convergence performance.

AB - Variance Minimization (VM) technique is one of the most popular methods for importance sampling (IS), but it has never been successfully applied to composite (generation and transmission) system reliability evaluation due to the difficulty of solving. In this paper, Geometric Programming (GP) is firstly introduced to repeatedly solve the VM optimization model in multi-levels to adaptively obtain the optimal IS parameters used in a IS-Monte Carlo Simulation (MCS) based composite system reliability evaluation considering rare events. Then, the IEEE Reliability Test System and its modified version are used to test the proposed methodology, and the proposed method is compared with another important technique for IS of Cross-Entropy (CE) in estimation accuracy and convergence performance.

KW - Geometric Programming (GP)

KW - Importance sampling (IS)

KW - Rare events

KW - Reliability evaluation

KW - Variance Minimization (VM)

UR - https://www.scopus.com/pages/publications/85001975401

U2 - 10.1109/PESGM.2016.7741903

DO - 10.1109/PESGM.2016.7741903

M3 - 会议稿件

AN - SCOPUS:85001975401

T3 - IEEE Power and Energy Society General Meeting

BT - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

PB - IEEE Computer Society

T2 - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

Y2 - 17 July 2016 through 21 July 2016

ER -

A multi-level adaptive GP-VM algorithm for composite system reliability evaluation considering rare events

Abstract

Publication series

Conference

Keywords

UN SDGs

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