Hanim Ismail - Academia.edu (original) (raw)

Papers by Hanim Ismail

Voltage sags are currently one of the vital issues in power quality today. Voltage sag is a short... more Voltage sags are currently one of the vital issues in power quality today. Voltage sag is a short duration reductions in RMS voltage caused by fault, induction motor starting and transformer energizing. Aim of this paper is to classify the caused of sag either by fault or induction motor starting using SVM. In this paper, the voltage sag was analyzed using PSCAD model. Then, a method to identify voltage sag using mother wavelet Daubechies 4 and support vector machines are used. The waves were discomposed into 10 levels using wavelet transform, afterwards, the selected energy features that were extracted from different levels, were employed as the inputs of the Support Vector Machines to classify the voltage sag.

Support Vector Machine (SVM), which is based on Statistical Learning theory, is a universal machi... more Support Vector Machine (SVM), which is based on Statistical Learning theory, is a universal machine learning method. This paper proposes the application of SVM in classifying the causes of voltage sag in power distribution system. Voltage sag is among the major power quality disturbances that can cause substantial loss of product and also can attribute to malfunctions, instabilities and shorter lifetime of the load. Voltage sag can be caused by fault in power system, starting of induction motor and transformer energizing. An IEEE 30 bus system is modeled using the PSCAD software to generate the data for different type of voltage sag namely, caused by fault and starting of induction motor. Feature extraction using the wavelet transformation for the SVM input has been performed prior to the classification of the voltage sag cause. Two kernels functions are used namely Radial Basis Function (RBF) and Polynomial function. The minimum and maximum of the wavelet energy are used as the input to the SVM and analysis on the performance of these two kernels are presented. In this paper, it has been found that the Polynomial kernel performed better as compared to the RBF in classifying the cause of voltage sag in power system.

Voltage sag is one of the major power quality disturbance today. Significant causes of voltage sa... more Voltage sag is one of the major power quality disturbance today. Significant causes of voltage sag include power system fault, starting of induction motor and energizing of transformer. This paper focus on a comparative analysis carried out to determine the best input parameters to be employed in support vector machine (SVM) method for voltage sag cause classification. Wavelet transform is carried out to extract the feature of these voltage sags which are used as the input to the SVM. Two different types of inputs used, namely the energy level and the min-max values of the wavelet. Comparisons between these input parameters are made to determine the best method which give the best prediction values. Training and testing data based on the standard IEEE 30 bus distribution system are simulated using PSCAD software. The results show that the performance of the min-max wavelet values as the input to the SVM is superior than the energy level input in term of accuracy and computational time.