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Forecasting and prediction applications in the field of power engineering

Booth, C.D. and McDonald, J.R. and McArthur, S.D.J. (2001) Forecasting and prediction applications in the field of power engineering. Journal of Intelligent and Robotic Systems, 31 (1-3). pp. 159-184. ISSN 0921-0296

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Abstract

Within the field of power engineering, forecasting and prediction techniques underpin a number of applications such as fault diagnosis, condition monitoring and planning. These applications can now be enhanced due to the improved forecasting and prediction capabilities offered through the use of artificial neural networks. This paper demonstrates the maturity of neural network based forecasting and prediction through four diverse case studies. In each case study the authors have developed diagnostic, monitoring or planning applications (within the power engineering field) using neural networks and industrial data. The engineering applications discussed in the paper are: condition monitoring and fault diagnosis applied to a power transformer; condition monitoring and fault diagnosis applied to an industrial gas turbine; electrical load forecasting; monitoring of the refuelling process within a nuclear power station. For each case study the data sources, data preparation, neural network methods and implementation of the resulting application is discussed. The paper will show that the forecasting and prediction techniques discussed offer significant engineering benefits in terms of enhanced decision support capabilities.

Item type: Article
ID code: 7029
Keywords: power engineering, artificial neural networks, forecasting, condition monitoring, fault diagnosis, gas turbine, transformer, nuclear reactor refuelling, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Professional Services > Corporate Services Directorate
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 16 Oct 2008
    Last modified: 16 Jul 2013 19:16
    URI: http://strathprints.strath.ac.uk/id/eprint/7029

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