Support Vector Regression Based S-transform for Prediction of Single and Multiple Power Quality Disturbances

Faisal , M. F. and Mohamed , Azah and Hussain, Aini and Nizam , Muhammad (2009) Support Vector Regression Based S-transform for Prediction of Single and Multiple Power Quality Disturbances. European Journal of Scientific Research , 34 (2). pp. 237-251. ISSN 1450-216X

[img] PDF - Published Version
Download (175Kb)

    Abstract

    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, SVR

    Item Type: Article
    Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
    Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam > Informatika
    Depositing User: mr admin 007
    Date Deposited: 20 May 2013 22:09
    Last Modified: 20 May 2013 22:09
    URI: https://eprints.uns.ac.id/id/eprint/753

    Actions (login required)

    View Item