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.
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References
Box, G. E. et al.: Time Series Analysis-Forecasting and Control, Holden-day, San Francisco, 1976.
Chen, W., Meherhomji, C. B., and Mistree, F.: Compromise-an effective approach for condition-based management of gas turbines, Engineering Optimization 22(3) (1994), 185-201.
Christiaanse,W.: Short termload forecasting using general exponential smoothing, IEEE Trans. Power Appl. Systems 90 (April 1971), 900-910.
Fayyad, U. M., Piatetshy-Shapiro, G. and Smyth, P.: From data mining to knowledge discovery: An overview, in: Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, 1996.
Franklin A. C. and Franklin, D. P.: The J & P Transformer Book, 11th edn, Butterworths, 1983.
Grimmelius, H. T. et al.: Three state-of-the-art methods for condition monitoring, IEEE Trans. Industrial Electronics 46(2) (1999), 407-416.
Haykin, S.: Neural Networks: A Comprehensive Foundation, Macmillan College Publishing, 1994.
Hiironniemi, E. et al.: Experiences of on-and off-line condition monitoring of power transformers in service, Paper 12-102, Cigre, August-September 1992.
Honig, M. and Messerschmitt, D.: Adaptive Filters, Structures, Algorithms, and Applications, Kluwer Academic, Hingham, MA, 1984.
Kirtley, J. L. et al.: Monitoring the health of power transformers, IEEE Computer Applications in Power, January 1996.
McArthur, S. D. J., Bell, S. C., McDonald, J. R., Mather, R., and Burt, S. M.: The application of model based reasoning within a decision support system for protection engineers, IEEE Trans. Power Delivery 11(4) (October 1996), 1748-1755.
McDowell, G. W. A. and Lockwood, M. L.: Real time monitoring of movement of transformer windings, in: IEE Colloquium on Condition Monitoring and Remnant Life Assessment in Power Transformers, 22 March 1994.
Park, D. C. et al.: Electric load forecasting using an artificial neural network, IEEE Trans. Power Systems (May 1991).
Toyoda, J. et al.: An application of state estimation to short term load forecasting, Part l: Forecasting modelling, Part 2: Implementation, IEEE Trans. Power Appl. Systems 89 (October 1970), 1678-1688.
Vemuri, S. et al.: Load forecasting using stochastic models, Paper no. TPIB, in: Proc. of 8th PICA Conf., Minneapolis, MN, 1973, pp. 31-37.
Vemuri, S. et al.: On-line algorithms for forecasting hourly loads of an electric utility, IEEE Trans. Power Appl. Systems 100 (August 1981), 3775-3784.
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Booth, C., McDonald, J.R. & McArthur, S.D.J. Forecasting and Prediction Applications in the Field of Power Engineering. Journal of Intelligent and Robotic Systems 31, 159–184 (2001). https://doi.org/10.1023/A:1012019526054
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DOI: https://doi.org/10.1023/A:1012019526054