Abstract
This paper proposes the use of a fast method of discriminating magnetic signals radiated from electrical discharges occurring in overhead lines string insulations. Two types of electrical discharges exist: dangerous that can lead to arcing discharges and those without danger that auto extinguish. We propose as a new method of discriminating and classifying partial discharges principal component analysis (PCA) combined with support vector machines (SVMs) which have proved their robustness in several disciplines. However, the used database is composed of two classes, the majority representing 130 harmless radiated magnetic field signals, while the second minority class represents 31 dangerous signals. The learning and test sets correspond respectively to 2/3 and 1/3 of the database. The SVMs application to the test set shows that no dangerous signal is detected, this being due to the fact that the two classes are unbalanced. We were then asked to apply the Principal Component Analysis (PCA) even before classification, which made it able to select the most relevant variables. The results show that by using PCA and then SVMs, the detection rate of a dangerous signal is 90%.
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
References
Aberkane, F., Moulai, H., Benyahia, F., Nacer, A., Beroual, A.: Pre-breakdown current discrimination diagnose technique for transformer mineral oil. In: IEEE Conference on Electrical Insulation and Dielectric Phenomena, 14–17 October 2012, Montreal, Quebec, Canada (2012)
Aberkane, F., Nacer, A., Moulai, H., Benyahia, F., Beroual, A.: ANN and multilinear regression line based discrimination technique between discharge currents for power transformers diagnosis. In: 2nd International Advances in Applied Physics and Material Science Congress, 26–29 April 2012, Antalya, Turkey (2012)
Aberkane, F., Moulai, H., Nacer, A., Benyahia, F., Beroual, A.: ANN and wavelet based discrimination technique between discharge currents in transformers mineral oil. Eur. Phys. J. Appl. Phys. 58, 20801 (2012)
Moulai, H.: Etude des Courants de Préclaquage dans les Diélectriques Liquides. Thèse de Doctorat d’Etat, Ecole Nationale Polytechnique, Alger, Algerie (2001)
Aberkane, F.: Etude des Processus de Décharges Electriques dans les Diélectriques Liquides. Université des Sciences et de la Technologie Houari Boumèdiene, Alger, Algérie, Thèse de Doctorat (2015)
Schenk, A.: Surveillance Continue des Transformateurs de Puissance par Réseaux de Neurones Auto-Organiséés. Thèse de Doctorat, Ecole Polytechnique Fédérale de Lausanne (2001)
Sanchez, J.: Aide au Diagnostic de défauts des Transformateurs de Puissance. Université de Grenoble, Thèse de Doctorat (2006)
Marques de Sá, J.P.: Pattern Recognition, Concepts, Methods and Applications, pp. 21–39. Springer, Heidelberg (2001), pp 147–239
Cheriet, M., Kharma, N., Liu, C.L., Suen, C.Y.: Character Recognition System, pp. 129–199. Wiley, New York (2007)
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimisation. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods-Support Vector Machines, pp. 185–208. MIT Press, Cambridge (1999). Chap 12
Abidine, M.B., Fergani, B., Oussalah, M., Fergani, L.: A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data. Kybernet J. 43(8), 1150–1164 (2014)
Webb, A.R.: Statistical Pattern Recognition, 2nd edn., pp. 319–329. Wiley, Hoboken (2002)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn., pp 114–115 (2002)
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. 6(1), 1–6 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gueraichi, M., Nacer, A., Moulai, H. (2019). Application of Principal Component Analysis (PCA) and SVMs for Discharges Radiated Fields Discrimination. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-01174-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01173-4
Online ISBN: 978-3-030-01174-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)