Prediction of flashover voltage of insulators using least squares support vector machine with particle swarm optimisation

• Prediction of flashover voltage of insulators using least squares support vector machine (LS-SVM) method.
• The swarm optimisation (PSO) is used to tune the LS-SVM parameters automatically.
• The obtained results are promising and insure that LS-SVM-PSO techniques can help High Voltage engineers.
• Satisfactory and more accurate results obtained by using the model to estimate the critical flashover voltage for the considered conditions compared with the previous works.

This paper describes the application of least squares support vector machine combined with particle swarm optimisation (LS-SVM-PSO) model to estimate the critical Flashover Voltage (FOV) on polluted insulators. The characteristics of the insulator: the diameter, the height, the creepage distance, the form factor and the equivalent salt deposit density were used as input variables for the LS-SVM-PSO model, and critical flashover voltage was estimated. In order to train the LS-SVM and to test its performance, the data sets are derived from experimental results obtained from the literature and a mathematical model. First, the LS-SVM regression model, with Radial Basis Function (RBF) kernel, is established. Then a global optimiser, PSO is employed to optimise the hyper-parameters needed in LS-SVM regression. Afterward, a LS-SVM-PSO model is designed to establish a nonlinear model between the above mentioned characteristics and the critical flashover voltage. Satisfactory and more accurate results are obtained by using LS-SVM-PSO to estimate the critical flashover voltage for the considered conditions compared with the previous works.