Critical flashover voltage on polluted insulators estimated using conventional and intelligent techniques

Summary

This paper proposes the use of conventional data analysis and computational intelligence methods for the estimation of critical flashover voltage on polluted Cap & Pin porcelain insulators. Specifically, modeling using Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) has been attempted, based on related application data. The database, used for the analysis, consists of 168 cases (i.e. series of related measurements) represented by six (6) numeric variables, namely, the diameter, the height, the creepage distance, the manufacturing constant, the pollution of insulators and the critical flashover voltage. Part of these data derives from a specialized data generation model corresponding to incidents of flashover voltage on polluted Cap & Pin insulators (simulation data), while the rest of the data consist of real experimental observations (real data). The comparison showed that ANNs are proved to be superior for modeling the estimation problem of the critical flashover voltage on polluted insulators. Finally, comparison between the results of this work with other similar approaches, previously existing in literature, shows that the results of the ANNs application are satisfactory, yet there is certainly still room for further improvement.

Additional informations

Authors

HALLER, Templeton, LUKAS, JAROSLAV

Keywords

Critical flashover voltage, insulators, artificial neural networks, multiple linear regression