Abstract
This paper introduces an approach to classifying faults in high voltage power system with a combination of rough sets and fuzzy sets in a neural computing framework. Typical error signals important for fault detection in power systems are considered. Features of these error signals derived earlier using Fast Fourier Transform analysis, amplitude estimation and waveform type identification, provide inputs to a neural network used in classifying faults. A form of rough neuron with memory is introduced in this paper. A brief overview of a rough-fuzzy neural computational method is given. The learning performance of a rough-fuzzy and pure fuzzy neural network are compared.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lingras, P.J.: Rough neural networks. In: Proc. of the 6th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU 1996), Granada, Spain, pp. 1445–1450 (1996)
Lingras, P.J., Osborne, P.: Unsupervised learning using rough Kohonen neural network classifiers. In: Proc. Symposium on Modelling, Analysis and Simulation (CESA 1996), Lille, France, pp. 753–757 (1996)
Lingras, P.J.: Comparison of neofuzzy and rough neural networks. In: Proc. of the 5th Int. Workshop on Rough Sets and Soft Computing (RSSC 1997), Durham, NC (March 1997)
Lingras, P.J.: Applications of rough patterns. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 2, pp. 369–384. Physica Verlag (a division of Springer Verlag), Hidleberg (1998)
Han, L., Menzies, R., Peters, J.F., Crowe, L.: High voltage power fault-detection and analysis system: Design and implementation. In: Proc. CCECE 1999, pp. 1253–1258.
Pawlak, Z.: Reasoning about data– A rough set persepective. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 25–34. Springer, Berlin (1998)
Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A New Trend in Decision-Making. pp. 3–98. Springer, Singapore (1999)
Pedrycz, W., Peters, J.F.: Learning in fuzzy Petri nets. In: Cardoso, J., Scarpelli, H. (eds.) Fuzziness in Petri Nets. Physica Verlag (a division of Springer Verlag), Hidleberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, L., Peters, J.F., Ramanna, S., Zhai, R. (1999). Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-48061-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
Online ISBN: 978-3-540-48061-7
eBook Packages: Springer Book Archive