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Your search keyword '"McArthur, Stephen D. J."' showing total 104 results
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104 results on '"McArthur, Stephen D. J."'

1. Knowledge and model based reasoning for power system protection performance analysis

Author

McArthur, Stephen D. J.

Subjects
621.31
Abstract

Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available.

Published
1996
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5. Prognostics and health management oriented data analytics suite for transformer health monitoring

Author

Aizpurua, Jose I., Stewart, Brian G., and McArthur, Stephen D. J.

Subjects
machine learning, data analytics, transformer health monitoring, anomaly detection, diagnostics, prognostics
Abstract

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring.

Published
2022

9. A Gaussian Process Based Fleet Lifetime Predictor Model for Unmonitored Power Network Assets

Author

Jiang, Xu, Stephen, Bruce, Chandarasupsang, Tirapot, McArthur, Stephen D. J., and Stewart, Brian G.

Abstract

This paper proposes the use of Gaussian Process Regression to automatically identify relevant predictor variables in a formulation of a remaining useful life model for unmonitored, low value power network assets. Reclosers are used as a proxy for evaluating the efficacy of this method. Distribution network reclosers are typically high-volume assets without on-line monitoring, leading to an insufficient understanding of which factors drive their failures. The ubiquity of reclosers, and their lack of monitoring, prevents the tracking of their individual remaining life, and, confirms their use in validating the proposed process. As an alternative to monitoring, periodic inspection data is used to evaluate asset risk level, which is then used in a predictive model of remaining useful life. Inspection data is often variable in quality with a number of features missing from records. Accordingly, missing inputs are imputed by the proposed process using samples drawn from an advanced form of joint distribution learned from test records and reduced to its conditional form. This work is validated on operational data provided by a regional distribution network operator, but conceptually is applicable to unmonitored fleets of assets of any power network.

Published
2023
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18. Automated feature validation of trip coil analysis in condition monitoring of circuit breakers

Author

Hosseini, Michael, Helm, Joseph, Stephen, Bruce, and McArthur, Stephen D. J.

Subjects
TK
Abstract

Datasets of historical performance metrics can offer valuable insight into an asset fleet’s health. This is especially so in the context to establishing normal behavior and thresholds of acceptable performance for diagnostic purposes. However, plant performance can often be obscured by data quality issues which introduce artefacts that do not pertain to asset health. This paper utilises a supervised ensemble machine-learning approach to automate the process of filtering maintenance data based on their predicted validity. The results are then presented both in terms of classification performance, and the impact on the distributions directly. This helps to ensure engineers are basing their diagnostic decisions on valid data. The accuracy of the filtration process, and its effect on the final thresholds will be discussed. To illustrate, this paper uses data of varying quality on circuit breaker trip tests obtained from operational medium-voltage circuit-breakers spanning several decades with the aim of providing decision support for switchgear diagnostics.

Published
2018

28. A Multi Agent System for Monitoring Industrial Gas Turbine Start-up Sequences

Author

Mangina, Eleni E., McArthur, Stephen D. J., McDonald, J. R., and Moyes, Alan

Subjects
Gas-turbines -- Analysis, Electrical machinery -- Monitoring, Business, Electronics, Electronics and electrical industries
Abstract

This paper introduces a novel technique for condition monitoring of gas turbine start up sequences. The vast amount of data and the complex processes behind on-line fault detection indicate the need for an automated solution. A multi agent system that views the problem as the interaction of simple independent software entities, for effective use of the available data, is presented. The overall solution is derived from the combination of partial solutions provided by the components of the multi-agent system. As a consequence, data interpretation is achieved by converting the data into appropriate information and combining individual agents' information, resulting in an automatic fault diagnosis for the engineers. This multi-agent system can employ various intelligent system techniques and has been implemented using the ZEUS Agent Building Toolkit. Index Terms--Condition monitoring, intelligent agents.

Published
2001

50. Automating the Analysis and Management of Power System Data using Multi-agent Systems Technology.

Author

Macintosh, Ann, Ellis, Richard, Allen, Tony, Davidson, Euan M., McArthur, Stephen D. J., McDonald, James R., Cumming, Tom, and Watt, Ian

Abstract

Earlier research [1] by the Institute of Energy and Environment and SP PowerSystems demonstrated that multi-agent systems technology could be used to integrate a number of software tools to automate the analysis and management of power systems monitoring data. This paper focuses on issues raised during the migration from an off-line prototype to an on-line multi-agent system for use at SP PowerSystems. The authors believe that the system described in this paper is one of the first of its type to be deployed in the electrical power industry for continual on-line use by operational engineers. [ABSTRACT FROM AUTHOR]

Published
2005
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