Wavelet based feature extraction for classification of Power Quality Disturbances
DOI:
https://doi.org/10.24084/repqj09.340Abstract
The detection and classification of power quality disturbances in power systems are important tasks in monitoring and protection of power system network. Most of the power system disturbances are non stationary and transitory in nature and new tools are being used nowadays for the analysis of power quality disturbances. This paper presents a wavelet based feature extraction method for the detection and classification of power quality disturbances. The disturbance waveforms obtained from simulation are decomposed by wavelet packet transform. The energy distribution pattern of the distorted signals has been chosen for feature extraction. Various power quality disturbances have been tested such as voltage sag, interruption, voltage swell, transient, flicker, low frequency and high frequency disturbances