Classification of power quality disturbances using wavelet transform: article

This paper explores the classification of power quality disturbances using wavelet transform techniques in electrical power systems. Power quality events, such as sags, swells, interruptions, and transients, can severely affect sensitive electronic equipment. To address this issue, the discrete wavelet transform is utilized to extract unique time-frequency features from disturbed voltage and current waveforms. These extracted features are then processed to automatically classify different types of power quality anomalies. The results demonstrate that the combination of wavelet transform for feature extraction provides a highly accurate, robust, and efficient approach for monitoring and diagnosing power quality problems in modern electrical grids.