UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY

Abstract

Purpose. The most common methods to design a multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power quality disturbances such as harmonic distortion, voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according to the energy deviation of the discrete wavelet transform. The proposed method gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform, this is good at recognizing and specifies the type of disturbance generated from the wind power generator.

Keywords

• unbalanced load flow
• wavelet transform (wt)
• support vector machines (svm)
• power quality disturbance
• wavelet energy