Recognition and classification of power quality event in power system using wavelet transformation

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, is conducted and on this base a novel approach based on wavelet transform (WT) to detect and locate the PQ disturbances is proposed. The signal containing noise is de-noised by wavelet transform to obtain a signal with higher signal-to-noise ratio, and then is input to the wavelet network; the standard genetic algorithm is used to fulfill the network structure; the fundamental component of the signal is estimated to extract the mixed information using wavelet network, and then the disturbance is acquired by subtracting the fundamental component; the principle of singularity detection using WT modulus maxima is presented and a dyadic wavelet transform approach for the detection and localization of the power quality disturbance is proposed. The simulation results demonstrate that the proposed method is effective.