Applying Multi-Agent System Technology in Practice: Automated Management and Analysis of SCADA and Digital Fault Recorder Data

Davidson, E.M.; McArthur, S.D.J.; McDonald, J.R.; Cumming, Tom; Watt, I.

doi:10.1109/tpwrs.2006.873109

Cited by 161 publications

(59 citation statements)

References 25 publications

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“…Systems reliant on rules and heuristics have a distinguished history of application in the condition monitoring of a wide variety of engineering scenarios: turbine generators [5], [6], power transformers [11], [12], and power system fault analysis [13]. The success in application of such systems reflects the advantages of knowledge-driven approaches, having the ability to provide clear conclusions and justification behind any decision support provided.…”

Section: A Related Workmentioning

confidence: 99%

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

et al. 2012

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This version is available at https://strathprints.strath.ac.uk/41099/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-This paper presents a self-tuning framework for the diagnosis of routine alarms in steam turbine generators utilizing a combination of inductive machine learning and knowledge-based heuristics. The techniques provide a novel basis for initializing and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine-specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm, and the applicability of systems using self-tuning techniques. The approaches discussed throughout are presented to provide useful diagnosis tools for the reliability and maintenance analysis of steam turbine generators.

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Section: A Related Workmentioning

confidence: 99%

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

et al. 2012

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“…These questions must be answered by analyzing the data generated by the fault, but there are different sets of data available. One utility, which arguably operates one of the most heavily monitored networks in Europe, performs post-fault analysis on four data sets: 6 • SCADA alarms that record automatic actions (such as high currents detected, main protection operating, and circuit-breakers triggering); • digital fault recorder (DFR) data, which records high-resolution current and voltage traces from a circuit when triggered by an incident; • traveling-wave fault locator data, which indicates how far along a line the fault is; and • circuit-breaker trip-coil current traces, indicating when the breaker was triggered and how long it took to operate.…”

Section: Post-fault Analysismentioning

confidence: 99%

Embedded Intelligence for Electrical Network Operation and Control

2011

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“…Moreover, the technology is maturing to the point where the first MAS are now being migrated from the laboratory to the utilities, allowing industry to gain experience in the use of MAS and also to evaluate their effectiveness [39]. A review of this large body of work has been made by Stephen et al [40].…”

Section: Communication and Control System Based On Masmentioning

confidence: 99%

Hierarchical MAS Based Control Strategy for Microgrid

Xiao

Huang

et al. 2010

Energies

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Abstract:Microgrids have become a hot topic driven by the dual pressures of environmental protection concerns and the energy crisis. In this paper, a challenge for the distributed control of a modern electric grid incorporating clusters of residential microgrids is elaborated and a hierarchical multi-agent system (MAS) is proposed as a solution. The issues of how to realize the hierarchical MAS and how to improve coordination and control strategies are discussed. Based on MATLAB and ZEUS platforms, bilateral switching between grid-connected mode and island mode is performed under control of the proposed MAS to enhance and support its effectiveness.

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Applying Multi-Agent System Technology in Practice: Automated Management and Analysis of SCADA and Digital Fault Recorder Data

Cited by 161 publications

References 25 publications

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

Embedded Intelligence for Electrical Network Operation and Control

Hierarchical MAS Based Control Strategy for Microgrid

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