2015•IET Generation Transmission & DistributionRequires access

Incorporation of protection system failures into bulk power system reliability assessment by Bayesian networks

Mojtaba Eliassi, Hossein Seifi, Mahmoud‐Reza Haghifam

Open publisher page 27 citations

Abstract

Although protection failures have critical influence on the reliability of power systems, the methodology of assessing composite power system reliability including protection failures has not gone far enough yet. In this study, a Bayesian network (BN)‐based analytical methodology is proposed for modelling and analysis of the impact of protection system failures on bulk power system reliability. Initially, basic BN model of composite power system reliability is constructed based on its minimal cutsets (MCs) and logical relationships between components, MCs and system failure. Then, different failure modes of protection system and the interactions among components caused by protection system failures are conveniently incorporated into the basic BN model and the reliability calculations. By using the presented method, several restrictive assumptions, implicit in the other methods, can be removed. Moreover, applying BN provides additional capabilities at modelling and analysis levels. The proposed method is applied to the IEEE reliability test system and the results demonstrate that the proposed method is effective and is flexible in applications.

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What this paper is about

Although protection failures have critical influence on the reliability of power systems, the methodology of assessing composite power system reliability including protection failures has not gone far enough yet. In this study, a Bayesian network (BN)‐based analytical methodology is proposed for modelling and analysis of the impact of protection system failures on bulk power system reliability. Initially, basic BN model of composite power system reliability is constructed based on its minimal cutsets (MCs) and logical relationships between components, MCs and system failure. Then, different failure modes of protection system and the interactions among components caused by protection system failures are conveniently incorporated into the basic BN model and the reliability calculations. By using the presented method, several restrictive assumptions, implicit in the other methods, can be removed. Moreover, applying BN provides additional capabilities at modelling and analysis levels. The proposed method is applied to the IEEE reliability test system and the results demonstrate that the proposed method is effective and is flexible in applications.

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OpenAlex reports 27 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Although protection failures have critical influence on the reliability of power systems, the methodology of assessing composite power system reliability including protection failures has not gone far enough yet. In this study, a Bayesian network (BN)‐based analytical methodology is proposed for modelling and analysis of the impact of protection system failures on bulk power system reliability. Initially, basic BN model of composite power system reliability is constructed based on its minimal cutsets (MCs) and logical relationships between components, MCs and system failure. Then, different failure modes of protection system and the interactions among components caused by protection system failures are conveniently incorporated into the basic BN model and the reliability calculations. By using the presented method, several restrictive assumptions, implicit in the other methods, can be removed. Moreover, applying BN provides additional capabilities at modelling and analysis levels. The proposed method is applied to the IEEE reliability test system and the results demonstrate that the proposed method is effective and is flexible in applications.

Key concepts: Reliability engineering, Reliability (semiconductor), Bayesian network, Electric power system, Computer science, Bayesian probability, Power (physics), Engineering

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