An Application of Multidimensional Scaling in Fault Detection of Smart Grids

Authors

  • Khaled Abdusamad Department of Mechanical Engineering, Garaboulli Engineering Faculty, Elmergib University, Libya
  • Tariq Aboalhol Higher Institute of Sciences and Technology, Yefren-Libya

Keywords:

Multidimensional scaling, MDS, fault detection, smart grid, cluster analysis

Abstract

Monitoring smart grids and detecting faults in such huge networks has recently become an active area of research. The huge amount of data transferred from the measurement units to the control center makes it difficult to detect faults in a reasonable amount of time. Some existing methods have been investigated and tested on many IEEE models such as wavelet transform, principal component analysis (PCA) to extract the abnormal behavior of signals under monitoring [1]. However, such techniques pose difficulties in detecting different faults properly. In this paper, the multidimensional scaling (MDS) is investigated as an alternative technique for reducing the dimensionality of the data to lower dimensions, while maintaining the necessary information needed for fault detection. MDS is then used to investigate the behavior of some IEEE models under different types of faults in order to detect and locate the faulty bus bars.

References

Jiang, Huaiguang, Jun Jason Zhang, and David W. Gao. "Fault localization in smart grid using wavelet analysis and unsupervised learning." In 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 386-390. IEEE, 2012.

Sood, Vijay K., Daniel Fischer, J. M. Eklund, and Tim Brown. "Developing a communication infrastructure for the smart grid." In 2009 IEEE Electrical power & energy conference (EPEC), pp. 1-7. IEEE, 2009.

Silva, K. M., Benemar A. Souza, and Nubia SD Brito. "Fault detection and classification in transmission lines based on wavelet transform and ANN." IEEE Transactions on Power Delivery 21, no. 4 (2006): 2058-2063.

Jiang, Joe-Air, Cheng-Long Chuang, Yung-Chung Wang, Chih-Hung Hung, Jiing-Yi Wang, Chien- Hsing Lee, and Ying-Tung Hsiao. "A hybrid framework for fault detection, classification, and location—part I: concept, structure, and methodology." IEEE Transactions on Power Delivery 26, no. 3 (2011): 1988-1998.

Arabie, Phipps, Mark S. Aldenderfer, Douglas Carroll, and Wayne S. DeSarbo. Three Way Scaling: A Guide to Multidimensional Scaling and Clustering. Vol. 65. Sage, 1987.

Lewandowsky, Stephan, and John C. Dunn. "Book Review: Multidimensional Scaling: History, Theory, and Applications". 1987

Schiffman, Susan S., M. Lance Reynolds, and Forrest W. Young. Introduction to multidimensional scaling. New York: Academic press, 1981.

Krustal, J. B., and Myron Wish. "Multidimensional scaling." Quantitative application in the social sciences. Beverly Hills, CA: Sage University Publications (1978).

Borg, Ingwer, and Patrick JF Groenen. Modern multidimensional scaling: Theory and applications. Springer Science & Business Media, 2005.

Mead, Al. "Review of the development of multidimensional scaling methods." Journal of the Royal Statistical Society: Series D (The Statistician) 41, no. 1 (1992): 27-39.

Nishimura, Kohei, Chisako Muramatsu, Mikinao Oiwa, Misaki Shiraiwa, Tokiko Endo, Kunio Doi, and Hiroshi Fujita. "Psychophysical similarity measure based on multi-dimensional scaling for retrieval of similar images of breast masses on mammograms." In Medical Imaging 2013: Computer- Aided Diagnosis, vol. 8670, p. 86701R. International Society for Optics and Photonics, 2013.

Yunus, Mohd, and Mohd Yusri. "Multivariate statistical process monitoring using classical multidimensional scaling." PhD diss., Newcastle University, 2012.

Bunke, Horst, Peter Dickinson, Andreas Humm, Ch Irniger, and Miro Kraetzl. "Computer network monitoring and abnormal event detection using graph matching and multidimensional scaling." In Industrial Conference on Data Mining, pp. 576-590. Springer, Berlin, Heidelberg, 2006.

Takane, Yoshio. "Matrices with special reference to applications in psychometrics." Linear algebra and its applications 388 (2004): 341-361.

Kruskal, Joseph B. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis." Psychometrika 29, no. 1 (1964): 1-27.

Kruskal, Joseph B. "Geometrical models and badness-of-fit functions." Multivariate analysis 2 (1969): 639-671.

De Leeuw, Jan, and Willem J. Heiser. "Multidimensional scaling with restrictions on the configuration." Multivariate analysis 5, no. 1 (1980): 501-522.

MacQueen, James. "Some methods for classification and analysis of multivariate observations." In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, no. 14, pp. 281-297. 1967.

Downloads

Published

2023-02-04