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Power quality (PQ) is becoming prevalent and of critical importance for power industry recently. The fast expansion in use of power electronics devices led to a wide diffusion of nonlinear, time-variant loads in the power distribution network, which cause massive serious power quality problems. The quantitative detection of two distortions of voltage waveform, i.e., voltage sag and voltage swell,...
To improve the precision of power quality disturbance detection and recognition in distributed power system, a novel method based on complex transform wavelet transform is presented. Due to the property that instantaneous amplitudes of voltages and currents as well as instantaneous phase differences can be obtained, the combined information with time and frequency localization properties are defined...
Recently, energy distribution of wavelet coefficient at different resolution level has been found to be very effective discriminatory feature for classification of power quality (PQ) disturbances. In practice, signals captured by monitoring devices are often corrupted by noise. The presence of noise will change the energy distribution pattern and may result in increased false classification rate....
In this paper, wavelets and fuzzy support vector machines are used to automated detect and classify power quality (PQ) disturbances. Electric power quality is an aspect of power engineering that has been with us since the inception of power systems. The types of concerned disturbances include voltage sags, swells, interruptions, switching transients, impulses, flickers, harmonics, and notches. Fourier...
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