Open AccessPerformance of different kernel functions for LS‐SVM‐GWO to estimate flashover voltage of polluted insulators
Author(s) -
Bessedik Sid Ahmed,
Djekidel Rabah,
Ameur Aissa
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2017.0486
Subject(s) - support vector machine , radial basis function kernel , kernel (algebra) , polynomial kernel , perceptron , radial basis function , voltage , least squares support vector machine , kernel method , variable kernel density estimation , engineering , computer science , mathematics , algorithm , artificial intelligence , mathematical optimization , artificial neural network , electrical engineering , discrete mathematics
This work attempts to clarify the potentials of hybrid model based on least‐squares support vector machine (LS‐SVM) and a novel meta‐heuristic algorithm called Grey Wolf optimiser (GWO) in high‐voltage applications, considering several kernel functions. The selection of the suitable kernel function and its parameters play an important role in the performance of LS‐SVM. For this purpose, GWO is proposed in this study as an efficient optimisation approach to adjust the parameters of various kernel functions such as linear kernel (Lin), radial basis function kernel, polynomial kernel (poly) and multi‐layer perceptron kernel. Afterwards, the LS‐SVM with the most appropriate kernel function is designed to model flashover voltage of polluted high‐voltage insulators. The performance of the developed model is compared with the previous works. The results confirm high capabilities of the proposed hybrid model for the prediction of the flashover voltage of polluted insulators.