Power quality disturbance detection and classification using wavelets and artificial neural networks
Branislava Perunicic-Drazenovic, M. Mallini, Z. Wang, Yang Liu · 2002
This article develops a method to detect and classify power quality problems using a novel combination of digital filtering, wavelets and artificial neural networks. The method is developed for voltage waveforms of arbitrary sampling rate and number of cycles, using a large variety of power quality events simulated with MATLAB(R) software, in addition to sampled waveforms from utility monitoring and EMTP(R) simulations. Power system monitoring, augmented by the ability to automatically characterize disturbed signals, is a powerful tool for the power system engineer to use in addressing power quality issues. This is a step toward the goal of automating the real-time monitoring, detection and classification of power signals.