Abstract
This paper copes with the problem of flashover voltage on polluted insulators, being one of the most important components of electric power systems. Α number of appropriately selected computational intelligence techniques are developed and applied for the modelling of the problem. Some of the applied techniques work as black-box models, but they are capable of achieving highly accurate results (artificial neural networks and gravitational search algorithms). Other techniques, on the contrary, obtain results somewhat less accurate, but highly comprehensible (genetic programming and inductive decision trees). However, all the applied techniques outperform standard data analysis approaches, such as regression models. The variables used in the analyses are the insulator’s maximum diameter, height, creepage distance, insulator’s manufacturing constant, and also the insulator’s pollution. In this research work the critical flashover voltage on a polluted insulator is expressed as a function of the aforementioned variables. The used database consists of 168 different cases of polluted insulators, created through both actual and simulated values. Results are encouraging, with room for further study, aiming towards the development of models for the proper inspection and maintenance of insulators.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Topalis, F.V., Gonos, I.F., Stathopulos, I.A.: Dielectric behaviour of polluted porcelain insulators. IEE Proc.-Gener. Transm. Distrib. 148(4), 269–274 (2001)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn., pp. 233–244. Elsevier Inc. (2001)
Refaeilzadeh, P., Tang, L., Liu, H.: Cross-Validation. Arizona State University (2008)
Crane, E.F., McPhee, N.F.: The effects of size and depth limits on tree based genetic programming. In: Yu, T., Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice III. Genetic Programming, vol. 9, pp. 223–240. Springer, Boston (2006). doi:10.1007/0-387-28111-8_15
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1994)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5, 239–266 (1990)
Mitchell, T.M.: Machine Learning, pp. 55–58. McGraw-Hill (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Karampotsis, E. et al. (2017). Computational Intelligence Techniques for Modelling the Critical Flashover Voltage of Insulators: From Accuracy to Comprehensibility. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-60042-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60041-3
Online ISBN: 978-3-319-60042-0
eBook Packages: Computer ScienceComputer Science (R0)