| Abstract |
Discrete wavelet transform (DWT) and artificial neural networks (ANN) are used for detecting,
compressing and classifying power quality disturbances. The method begins by decomposing an input
signal into its details, and its most-smoothed signal. The detail signals contain wavelet transform
coefficients (WTC). Thresholding of WTC permits selection of those corresponding to disturbance
events. To recover the input signal, reconstruction is performed using the most-smoothed signal,
along with the saved WTC of the detail signals. Data are stored with a high compression ratio, while
the error between the input and the reconstructed signals is minimized. Eight types of actual power
line disturbances are classified using an ANN structure. |
