Abstract
This paper presents an adaptive neuro-fuzzy inference system and a set of novel features for classification of power quality disturbances. The most common types of disturbances including flickers, harmonics, impulses, notches, outages, sags, swells, and switching transients are considered in this research. The proposed method employs voltage waveforms for analysis. The features are extracted utilizing the signal processing techniques such as the windowed discrete Fourier transform and S-transform. Evaluation studies based on both simulated and field data are reported.
Original language | English |
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Article number | 4 |
Journal | International Journal of Emerging Electric Power Systems |
Volume | 10 |
Issue number | 3 |
DOIs | |
State | Published - 2009 |
Keywords
- Adaptive neuro-fuzzy inference system
- Decision making
- Feature extraction
- Power quality disturbances
- S-transform
ASJC Scopus subject areas
- Energy Engineering and Power Technology