First Order Statistical Features Thermal Images for Surge Arrester Fault Classification

Yusuf Novizon; Zulkurnain Abdul-Malek

doi:10.4028/www.scientific.net/AMM.818.91

Paper Titles

Review on Dissolved Fault Gases in Monitoring Bio-Oil Filled Transformer
p.69

Assessment of Series Compensator for Ferroresonance Damping in Power System
p.74

Nonlinear Resonance Suppression as Distorting and Saturating Source in Voltage and Current of Power Transformer: UPFC and STATCOM Performance Evaluation
p.79

Investigation on Equipment Failure Mode and Causes Using Infra-Red Thermovision Camera Images for High Voltage Electrical Connection
p.86

First Order Statistical Features Thermal Images for Surge Arrester Fault Classification
p.91

Neutral Networks for Fault Classification: Comparison between Feed-Forward Back-Propagation, RBF and LVQ Neural Network
p.96

Assessment of Series Compensator for Ferroresonance Damping in Power System
p.101

Design and Model Analysis of Pulse Generator in Electrical Discharge Machines (EDM) System Using in the Laplace Transform
p.106

Simulation and Analysis of RC Type Relaxation Power Generator for Electrical Discharge Machines (EDM) in Laplace Transform
p.112

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 818First Order Statistical Features Thermal Images...

First Order Statistical Features Thermal Images for Surge Arrester Fault Classification

Abstract:

— Thermal imaging technique is a very convenient, versatile and non-contact method which has been used for fault condition diagnosis of electrical equipment. The fault condition diagnosis is composed with data acquisition, data pre-processing, data analysis and decision making. Some important features contain in thermal image can be extracted for equipment condition monitoring and fault diagnosis. This paper attempts to extract some important features from the zinc oxide (ZnO) surge arrester using first order statistical histogram extraction to classify the fault condition using neural network. The experimental work was carried out to capture thermal image of 120 kV rated ZnO surge arrester. The thermal images were segmented and plotted histogram using dedicated software. Some features such as the maximum, minimum, mean, standard deviation, and variance were extracted using the extraction method, classification of aging was carried out using the neural network based on the leakage current values. The health states consist of normal, defection and faulty. The results show that the thermal image features extracted using the extraction method can be used to classify the fault condition of the ZnO surge arresters

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 818)

Pages:

91-95

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.818.91

Citation:

Cite this paper

Online since:

January 2016

Authors:

Yusuf Novizon, Zulkurnain Abdul-Malek*

Keywords:

Classification, Feature Extraction, First Order Statistical, Neural Network, Thermal Image, Zinc Oxide Surge Arrester

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] S. Chudzik, Measurement of thermal parameters of a heat insulating material using infrared thermography, Infrared Physics & Technology, vol. 55, pp.73-83, 1/ (2012).

DOI: 10.1016/j.infrared.2011.09.005

Google Scholar

[2] J. Chen, L. Han, and C. Whan, Method of information extraction based on infrared target recognition, Energy Procedia, vol. 13, pp.1548-1553, (2011).

Google Scholar

[3] Abdul-Malek Z., Khavari A. H., Nabipour-Afrouzi H., Effect of Ambient Temperature Zinc Oxide Surge Arrester Condition Monitoring, Applied Mechanics and Materials, Vol. 554 (2014), pp.573-577. doi. 10. 4028/www. scientific. net/AMM. 554. 573.

DOI: 10.4028/www.scientific.net/amm.554.573

Google Scholar

[4] Novizon, Abdul-Malek Z., Bashir N., N. Asilah, Thermal Image and Leakage Current Diagnostic as a Tool for Testing and Condition Monitoring of ZnO Surge Arrester, Jurnal Teknologi, 64: 4 (2013), 27-32. doi. org/10. 11113/jt. v64. (2096).

DOI: 10.11113/jt.v64.2096

Google Scholar

[5] Novizon, Abdul-Malek Z., Correlation between Third Harmonic Leakage Current and Thermography Image of Zinc Oxide Surge Arrester for Fault Monitoring Using Artificial Neural Network, Applied Mechanics and Materials, Vol. 554 (2014).

DOI: 10.4028/www.scientific.net/amm.554.598

Google Scholar

[6] J. -p. Sun, W. Chen, F. -y. Ma, F. -z. Wang, L. Tang, and Y. -j. Liu, Classification of Infrared Monitor Images of Coal Using an Feature Texture Statistics and Improved BP Network, Journal of China University of Mining and Technology, vol. 17, pp.489-493, 12/ (2007).

DOI: 10.1016/s1006-1266(07)60131-x

Google Scholar

[7] D. Formenti, N. Ludwig, M. Gargano, M. Gondola, N. Dellerma, A. Caumo, et al., Thermal Imaging of Exercise-Associated Skin Temperature Changes in Trained and Untrained Female Subjects, Annals of Biomedical Engineering, vol. 41, pp.863-871, 2013/04/01 (2013).

DOI: 10.1007/s10439-012-0718-x

Google Scholar

[8] A. Sengur and Y. Guo, Color texture image segmentation based on neutrosophic set and wavelet transformation, Computer Vision and Image Understanding, vol. 115, pp.1134-1144, (2011).

DOI: 10.1016/j.cviu.2011.04.001

Google Scholar

[9] T. D'Orazio, C. Guaragnella, M. Leo, and P. Spagnolo, Defect detection in aircraft composites by using a neural approach in the analysis of thermographic images, NDT & E International, vol. 38, pp.665-673, 12/ (2005).

DOI: 10.1016/j.ndteint.2005.04.005

Google Scholar

[10] N. Sang and T. Zhang, Segmentation of FLIR images by Hopfield neural network with edge constraint, Pattern Recognition, vol. 34, pp.811-821, 4/ (2001).

DOI: 10.1016/s0031-3203(00)00041-8

Google Scholar

[11] W. T. Chan, K. S. Sim, and C. P. Tso, Application of optical character recognition in thermal image processing, Infrared Physics & Technology, vol. 54, pp.353-366, (2011).

DOI: 10.1016/j.infrared.2011.04.001

Google Scholar

[12] M. Naka, T. Imai, T. Shida, M. Sato, R. Ito, and I. Akamine, Thermal image processing using neural network, " in Neural Networks, 1993. IJCNN , 93-Nagoya. Proceedings of 1993 International Joint Conference on, 1993, pp.2065-2068 vol. 3.

DOI: 10.1109/ijcnn.1993.714129

Google Scholar

[13] K. Yang, Z. Xue, H. Li, T. Jia, and Y. Cui, New methodology of hyperspectral information extraction and accuracy assessment based on a neural network, Mathematical and Computer Modelling, vol. 58, pp.644-660, 8/ (2013).

DOI: 10.1016/j.mcm.2011.10.037

Google Scholar