Synthetic data of the paper: “Quantification of feature importance in automatic classification of power quality distortions”

This dataset is related to the paper “Quantification of feature importance in automatic classification of power quality distortions” (IEEE International Conference on Harmonics and Quality of Power, March 2020). It includes the features extracted from synthetic signals with power quality distortions obtained from a public model (doi: 10.1109/ICHQP.2018.8378902).The dataset includes data associated with 8 features (described in the paper above) obtained from signals processed with the Stockwell Transform. The distortions considered in the paper were: normal signal, sag, swell, interruption, oscillatory transient, harmonics, sag with harmonics, swell with harmonics and flicker. One hundred and fifty samples are included for each distortion and feature. Different Signal-to-Noise Ratios (SNRs) are included in the dataset: noiseless, 50 dB, 40 dB, 30 dB and 20 dB.