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This paper presents a method based on S-transform and artificial neural network for detection and classification of power quality disturbances. The input features of the neural network are extracted using S-transform. The features obtained from the S-transform are distinct, understandable and immune to noise. These features after normalization are given to a feed forward neural network trained by...
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,...
A novel approach for the power quality (PQ) disturbances classification based on the wavelet transform and self-organizing learning array system is proposed. Wavelet network is utilized to extract feature vectors for various PQ disturbances and the wavelet transform can accurately localizes the characteristics of a signal both in the time and frequency domains. These feature vectors then are applied...
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