This paper presents a novel approach using Support Vector Regression (SVR) based
S-transform to predict the classes of single and multiple power quality disturbances in a
three-phase industrial power system. Most of the power quality disturbances recorded in an
industrial power system are non-stationary and comprise of multiple power quality
disturbances that coexist together for only a short duration in time due to the contribution
of the network impedances and types of customers’ connected loads. The ability to detect
and predict all the types of power quality disturbances encrypted in a voltage signal is vital
in the analyses on the causes of the power quality disturbances and in the identification of
incipient fault in the networks. In this paper, the performances of two types of SVR based
S-transform, the non-linear radial basis function (RBF) SVR based S-transform and the
multilayer perceptron (MLP) SVR based S-transform, were compared for their abilities in
making prediction for the classes of single and multiple power quality disturbances. The
results for the analyses of 651 numbers of single and multiple voltage disturbances gave
prediction accuracies of 86.1% (MLP SVR) and 93.9% (RBF SVR) respectively.
Keywords: Power Quality, Power Quality Prediction, S-transform, SVM, SV
