PQ Issues Dataset Using Short Time Fourier Transform

Power Quality (PQ) is now one of the most important issues of a modern power system because of the increased reliance on sensitive electrical and electronic equipment, integration of renewable energy, and sophisticated industrial loads. In order to identify the type of PQ issues in the system using AI techniques, there is a need for data. The dataset required to train AI models is developed using the Short Time Fourier Transform (STFT) technique. While developing the PQ dataset using STFT, we consider a total of 5 issues, i.e., sag, swell, interruption, transients, and harmonics. For each issue, 100 samples of data with 2091 features are extracted using STFT. This is the original dataset. Later, we reduced the number of features based on standard deviation, and that reduced dataset has a total of 792 features for each issue. Further, the dataset is reduced in terms of features based on correlation. And that optimal dataset has 500 samples with 44 features. Each PQ issue is represented with a numeric value. 0: Sag, 1: Swell, 2: Harmonics, 3: Interruption, 4: Transients