Abstract
Distribution transformer losses constitute a significant amount of the total losses in distribution networks. In Greece, for example, it is estimated that transformer iron losses are about 10% of the total distribution network losses [1]. In an industrial environment, dealing with construction of wound core distribution transformers, prediction of iron losses of individual cores is a crucial task, since the latter significantly affect both the quality and the performance of the finally produced three phase transformers. However, there is no simple analytical relationship for estimating iron losses of individual cores, due to the fact that many parameters, both qualitative and quantitative, are involved in the process.
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References
Papadopoulos M 1994 Electric Energy Distribution Networks. NTUA, Athens (in Greek)
Beal R, Jackson T 1990 Neural Computing: An Introduction. IOP Publishing Ltd., Bristol, UK
Haykin S 1994 Neural Networks:A Comprehensive Foundation. Macmillan, New York
Peng T M, Hubele N F, Karady G G 1992 Advancement in the Application of Neural Networks for Short-Term Load Forecasting. IEEE Trans, on Power Systems 7:250–257
Lu C N, Wu H T, Vemuri S 1993 Neural Network Based Short Term Load Forecasting. IEEE Trans, on Power Systems 8:336–342
Park D C, El-Sharkawi M A, Marks R J II, Atlas L E, Dambong M J 1991 Electric Load Forecasting Using an Artificial Neural Network. IEEE Trans, on Power Systems 6:442–448
Pecas-Lopes J A, Fidalgo J N, Miranda V, Hatziargyriou N 1994 Neural Networks Used for On-Line Dynamic Security Assessment of Isolated Power Systems with a Large Penetration from Wind Production - A Real Case Study. In: 3 rd International Workshop on Rough Sets and Soft Computing (RSSC’94). San Jose, California
Sobajic D J, Pao Y H 1989 Artificial Neural-Net Based Dynamic Security Assessment for Electric Power Systems. IEEE Trans. On Power System 4:220–228
Weerasooriya S, El-Sharkawi M A, Dambong M, Marks R J II 1992 Towards Static Security Assessment of a Large Scale Power System Using Neural Networks.IEE Proc., Part C 139:64–70
Georgilakis P S, Bakopoulos J A, Hatziargyriou N D 1997 A Decision Tree Method for Prediction of Distribution Transformer Iron Losses. In: 32 nd Universities Power Engineering Conference (UPEC ’97), Vol. 1. Manchester, pp 257–260
AI Ware 1989 NNET 210 User’s Manual. AI Ware Incorporated, Cleveland, OH
Kollias S, Anastassiou D 1989 An Adaptive Least Squares Algorithm for the Efficient Training of Artificial Neural Networks. IEEE Trans, on Circuits & Systems 36:1092–1101
Kollias S 1996 A Multiresolution Neural Network Approach to Invariant Image Recognition. Neurocomputing 12: 35–57
Taguchi G, Konishi S 1987 Taguchi Methods: Orthogonal Arrays and Linear Graphs; Tools for Quality Engineering. ASI, Dearborn, MI, USA
Logothetis N 1992 Managing for Total Quality. Prentice Hall International, UK
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Georgilakis, P.S., Hatziargyriou, N.D., Doulamis, N.D., Doulamis, A.D., Bakopoulos, J.A. (1999). An Efficient PC-Based Environment for the Improvement of Magnetic Cores Industrial Process. In: Advances in Manufacturing. Advanced Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-0855-9_34
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DOI: https://doi.org/10.1007/978-1-4471-0855-9_34
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1217-4
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