I.M. Elders
University of Strathclyde
51 Papers
399 Citations
I.M. Elders is an academic researcher from University of Strathclyde. The author has contributed to research in topics: Electric power system & Fault (power engineering). The author has an hindex of 13, co-authored 47 publications.
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Papers
Diagnosis of Series DC Arc Faults—A Machine Learning Approach
TL;DR: This paper proposes the IntelArc system to accurately diagnose series arc faults in dc systems, which combines time–frequency and time-domain extracted features with hidden Markov models (HMMs) to discriminate between nominal transient behavior and arc fault behavior across a variety of operating conditions.
Translating CIM XML power system data to a proprietary format for system simulation
A.W. McMorran,G. AuIt,I.M. Elders,C.E.T. Foote,Graeme Burt,J.R. McDonald +5 more
- 06 Jun 2004
TL;DR: The basis of this paper is that the EPRI common information model (CIM) in eXtensible Markup Language (XML) represents the first stage in a revolution of data exchange and manipulation for power systems.
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Translating CIM XML power system data to a proprietary format for system simulation
TL;DR: The work in this article explores the problem of translating data in the CIM XML format to the required format for such legacy power system analysis applications, and discusses solutions to some of the challenges in data translation.
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Electricity Network Scenarios for Great Britain in 2050
I.M. Elders,Graham Ault,Stuart Galloway,J.R. McDonald,Jonathan Köhler,Matthew Leach,Efterpi Lampaditou +6 more
TL;DR: In this article, the authors developed and presented six possible future electricity industry scenarios for Great Britain, focussed on the year 2050, and drew upon discussions of important technologies presented by expert authors in other chapters to consider the impact of different combinations of key influences on the nature of the power system in 2050.
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A common information model (CIM) toolkit framework implemented in Java
TL;DR: This paper discusses solutions to some of the challenges in storing and processing large power system network models as native Java objects without sacrificing reliability and robustness and addresses the issue of data processing performance in contrast to other approaches.
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