Adaptive wavelet networks for power-quality detection and discrimination in a power system

This paper proposes a model of power-quality detection for power system disturbances using adaptive wavelet networks (AWNs). An AWN is a two-subnetwork architecture, consisting of the wavelet layer and adaptive probabilistic network. Morlet wavelets are used to extract the features from various disturbances, and an adaptive probabilistic network analyzes the meaningful features and performs discrimination tasks. AWN models are suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The proposed AWN has been tested for the power-quality problems, including those caused by harmonics, voltage sag, voltage swell, and voltage interruption. Compared with conventional wavelet networks, the test results showed accurate discrimination, fast learning, good robustness, and faster processing time for detecting disturbing events.