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The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome...
This paper proposed a power quality disturbances classification system based on wavelet transforms and novel probabilistic neural network (PNN). Wavelet transform is utilized to extract feature vectors for various power quality disturbances based on multi-resolution analysis. The decomposition signal is divided into 5 equal length bins in each level. Root mean square (RMS) value of the wavelet coefficients...
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