Integrated spline wavelet and Kalman filter approach for power quality monitoring in a power network

Existing techniques for recognizing and identifying sag, swell and momentary interruption are based on visual inspection of the waveform. It is the purpose of this paper to apply technological advances, especially in wavelet transforms and Kalman filtering approach to the problem of automatic disturbance (sag, swell, momentary interruption) and waveform classification. Unlike past attempts to automatically identify disturbance waveforms where identification is performed in the time domain using an individual artificial neural network, the proposed recognition scheme is carried out in the wavelet domain using Kalman filtering approach. Several computational tests are performed using power quality disturbance signals clearly showing the efficiency of this new integrated approach.