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Probabilistic Relational Models for assessment of reliability of active distribution management systems
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-3014-5609
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.ORCID iD: 0000-0003-3922-9606
2010 (English)In: 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010, Singapore: IEEE , 2010, 454-459 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the use of Probabilistic Relational Models (PRM) for reliability analysis of control systems for active distribution grids. The approach is based on two key concepts; first, it addresses both the reliability of primary system components and the supporting secondary, ICT-based systems. Secondly, the use of PRMs enables representation of architecture of the ICT systems, including for instance redundancy of hardware and allocation of software functions to several hardware devices. This later aspect is important, since allocation of software across different hardware platforms is a feature enabled by for instance the IEC 61850 standard. The increasing number of software dependent systems for controlling and supervising the power grid enhances the risk of software-caused failures. Thus, for reliable operation it is of high importance to not only concern primary component, but also the software and hardware of the secondary systems controlling it. A variety of methods exist for reliability analysis of secondary systems, however few address the issue of failing software together with failing primary components. The paper presents the underlying theory for Probabilistic Relational Models, and presents the steps necessary to use the technique. The paper is concluded with an example of application of the approach.

Place, publisher, year, edition, pages
Singapore: IEEE , 2010. 454-459 p.
Keyword [en]
Active distribution grids, Bayesian networks, Component, Control and automation systems, Probabilistic relational models, Reliability analysis, Bayesian, Distribution grid, Control system analysis, Distributed parameter networks, Inference engines, Intelligent networks, Power generation, Probability distributions, Software reliability, Speech recognition, Quality assurance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-49937DOI: 10.1109/PMAPS.2010.5528965Scopus ID: 2-s2.0-77956427067ISBN: 978-142445723-6 (print)OAI: oai:DiVA.org:kth-49937DiVA: diva2:460681
Conference
2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010; Singapore; 14 June 2010 through 17 June 2010
Note
QC 20111201Available from: 2011-11-30 Created: 2011-11-30 Last updated: 2014-05-05Bibliographically approved
In thesis
1. Analyzing Substation Automation System Reliability using Probabilistic Relational Models and Enterprise Architecture
Open this publication in new window or tab >>Analyzing Substation Automation System Reliability using Probabilistic Relational Models and Enterprise Architecture
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Modern society is unquestionably heavily reliant on supply of electricity. Hence, the power system is one of the important infrastructures for future growth. However, the power system of today was designed for a stable radial flow of electricity from large power plants to the customers and not for the type of changes it is presently being exposed to, like large scale integration of electric vehicles, wind power plants, residential photovoltaic systems etc. One aspect of power system control particular exposed to these changes is the design of power system control and protection functionality. Problems occur when the flow of electricity changes from a unidirectional radial flow to a bidirectional. Such an implication requires redesign of control and protection functionality as well as introduction of new information and communication technology (ICT). To make matters worse, the closer the interaction between the power system and the ICT systems the more complex the matter becomes from a reliability perspective. This problem is inherently cyber-physical, including everything from system software to power cables and transformers, rather than the traditional reliability concern of only focusing on power system components.

The contribution of this thesis is a framework for reliability analysis, utilizing system modeling concepts that supports the industrial engineering issues that follow with the imple-mentation of modern substation automation systems. The framework is based on a Bayesian probabilistic analysis engine represented by Probabilistic Relational Models (PRMs) in com-bination with an Enterprise Architecture (EA) modeling formalism. The gradual development of the framework is demonstrated through a number of application scenarios based on substation automation system configurations.

This thesis is a composite thesis consisting of seven papers. Paper 1 presents the framework combining EA, PRMs and Fault Tree Analysis (FTA). Paper 2 adds primary substation equipment as part of the framework. Paper 3 presents a mapping between modeling entities from the EA framework ArchiMate and substation automation system configuration objects from the IEC 61850 standard. Paper 4 introduces object definitions and relations in coherence with EA modeling formalism suitable for the purpose of the analysis framework.

Paper 5 describes an extension of the analysis framework by adding logical operators to the probabilistic analysis engine. Paper 6 presents enhanced failure rates for software components by studying failure logs and an application of the framework to a utility substation automation system. Finally, Paper 7 describes the ability to utilize domain standards for coherent modeling of functions and their interrelations and an application of the framework utilizing software-tool support.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. xiii, 44 p.
Series
TRITA-EE, ISSN 1653-5146 ; 2014:021
Keyword
Reliability analysis, substation automation, Enterprise Architecture, probabilistic analysis, Probabilistic Relational Models, Bayesian networks, software reliability, failure rates, fault tree analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-145006 (URN)978-91-7595-131-7 (ISBN)
Public defence
2014-05-19, Q2, Osquldas väg 10, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20140505

Available from: 2014-05-05 Created: 2014-05-05 Last updated: 2014-05-05Bibliographically approved

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Nordström, LarsEkstedt, Mathias

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