Optimal Selection of Sampling Rates and Mother Wavelet for an Algorithm to Classify Power Quality Disturbances

The introduction of renewable energy sources, distributed energy systems, and power electronics equipment has led to the emergence of the Smart Grid. However, these developments have also caused the worsening of power quality. Selecting the correct sampling frequency and feature extraction techniques are essential for appropriately analyzing power quality disturbances. This work compares the performance of an algorithm based on a Support Vector Machine and Discrete Wavelet Transform for the classification of power quality disturbances using eight sampling rates and five different mother wavelets. The algorithm was tested in noisy and noiseless scenarios to show the methodology. The results indicate that a success rate of 99.9% is obtained for the noiseless signals using a sampling rate of 9.6 kHz and 95.2% for signals with a signal-to-noise ratio of 30 dB with a sampling rate of 30 kHz.