Fuzzy Data Mining as a Tool to Infer Pollution Severity of Power Transmission Line System

K. Karpagavani; A. Kumaravel

doi:10.6180/jase.2013.16.1.11

Fuzzy Data Mining as a Tool to Infer Pollution Severity of Power Transmission Line System

K. Karpagavani This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and A. Kumaravel²

¹Department of Sciences, Sona College of Technology, Salem-636005, Tamilnadu, India
²Department of Mechanical Engineering, K.S. Rangasamy College of Technology, Tiruchengode-637215, Tamilnadu, India

Received: March 2, 2012
Accepted: October 29, 2012
Publication Date: March 1, 2013

Download Citation: ||https://doi.org/10.6180/jase.2013.16.1.11

ABSTRACT

Flashover of power transmission line due to polluted insulators leads to failure of equipments, catastrophic fires and power outages. This paper deals with the development of pollution severity diagnostic system of power transmission line insulators based on the fuzzy data mining approach. In this work, laboratory based experiments were carried out on power transmission line porcelain insulators under AC voltages at different pollution conditions and corresponding leakage current patterns were monitored. Variation of important features of leakage current such as leakage current magnitude, total harmonic distortion and third harmonic content at different pollution levels were analyzed. Fuzzy C-means algorithm is used to cluster the extracted features of the leakage current data. Test results clearly shows that the fuzzy clustering technique can effectively realize the surface pollution condition of the power transmission line insulators and can be useful to carry out preventive maintenance work.

Keywords: Insulator, Power Systems, Leakage Current, FFT, THD, Fuzzy C-Means

REFERENCES

[1] Gorur, R. S., Cherney, E. A. and Burnham, J. T., Outdoor Insulators, Ravi S. Gorur, Inc., Phoenix, Arizona 85044, USA (1999).
[2] Looms, J. S. T., Insulators for High Voltages, IEE Series (1990).
[3] Boudissa, R., et al., “Effect of Insulator Shape on Surface Discharges and Flashover under Polluted Conditions,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 12, No. 3, pp. 429436 (2005).
[4] Chandrasekar, S., Kalaivanan, C., Andrea Cavallini and Gian Carlo Montanari, “Partial Discharge Detection as a Tool to Infer Pollution Severity of Polymeric Insulators,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 17, No. 1, pp. 181188 (2010).
[5] Swain, N. K., Anderson, J. A., Singh Ajit, Swain, M., Fulton, M., Garrett, J. and Tucker, O., “Remote Data Acquisition, Control and Analysis Using LabVIEW Front Panel and Real Time Engine,” Proc. of IEEE Southeast Conference, pp. 16 (2003).
[6] Karpagavani, K. and Kumaravel, A., “Design of a Web Based Condition Monitoring System for Transmission Line Applications,” Journal of Communication and Computer, Vol. 7, No. 7, pp. 2027 (2010).
[7] Babnik, T., Aggarwal, R. and Moore, P., “Data Mining on a Transformer Partial Discharge Data Using the Self-Organizing Map,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 14, No. 2, pp. 444452 (2007).
[8] Lin, H. J., Yang, F. W. and Kao, Y. T., “An Efficient GA-based Clustering Technique,” Tamkang Journal of Science and Engineering, Vol. 8, pp. 113122 (2005).
[9] IEC 60507, Artificial Pollution Tests on High Voltage Insulators to be Used on AC Systems (1991).
[10] Naderian, A., Sanaye-Pasand, M. and Mohseni, “A Review of Artificial Contamination Test Methods of High Voltage Outdoor Insulators,” IEEE Int. Symposium on Electrical Insulation, pp. 284287, USA (2004).
[11] El-Hag, A. H., Jayaram, S. H. and Cherney, E. A., “Fundamental and Low Frequency Harmonic Components of Leakage Current as a Diagnostic Tool to Study Aging of RTV and HTV Silicone Rubber in Salt Fog,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 10, No. 1, pp. 128136 (2003).
[12] Chandrasekar, S., Kalaivanan, C., Cavallini, A. and Gian Carlo Montanari, “Investigations on Leakage Current and Phase Angle Characteristics of Porcelain and Polymeric Insulator under Contaminated Conditions,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 16, No. 2, pp. 574583 (2009).
[13] Gil, M., Sarabia, E. G., Llata, J. R. and Oria, J. P., “Fuzzy C-Means Clustering for Noise Reduction, Enhancement, Reconstruction of 3D Ultrasonic Images,” Proceedings of IEEE International Conference on Emerging Technologies and Factory Automation, pp. 465472 (1999).
[14] Chen, Y. S., Chen, B. T. and Hsu, W. H., “Efficient Fuzzy C-Means Clustering for Image Data,” Journal of Electronic Imaging, Vol. 14, Issue 1, 013017, pp. 113 (2005).
[15] Halkidi, M., Batistakis, Y. and Vazirgiannis, M., “On Clustering Validation Techniques,” Journal of Intelligent Information Systems, Vol. 17, pp. 107145 (2001).