Abstract—Voltage sag disturbance can cause catastrophe failures to both utility and end users of electrical power. The causes of this disturbance include power system fault condition and induction motor starting. This paper presents a research to classify the causes of voltage sag by employing Multi Resolution Analysis (MRA) and Support Vector Machines (SVM). Voltage sag data are obtained from the implementation of various fault conditions and induction motor starting using PSCAD modeling and data generation. Multi Resolution Analysis (MRA) is developed as a representation of a signal with various levels of decomposition for the features extraction. The two features used are the minimum and maximum of voltage and the energy distribution of ten decomposition levels of the MRA. Those features are used as the input for classification using Support Vector Machines (SVM). In classification part, the Radial Basis Function (RBF) kernel of the SVM has been used. The result shows that the MRA and SVM can classify the source of voltage sag with promising accuracy.
Index Terms—Power quality, voltage sag, multi resolution analysis, support vector machine.
H. Ismail, Z. Zakaria, and N. Hamzah are with Universiti Teknologi MARA, Malaysia (e-mail: hanim_ismail@yahoo.com, noralizah@salam.uitm.edu.my, zuhainaz@salam.uitm.edu.my).
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Cite:Hanim Ismail, Zuhaina Zakaria, and Noraliza Hamzah, "Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine," Journal of Clean Energy Technologies vol. 4, no. 3, pp. 183-186, 2016.
