Simulation analysis of time-frequency based on waveform detection technique for power quality application

The automatic detection and classification of power quality disturbances has become a significant issue in modern power industry, because of electric load sensitive to power transient signal. This paper presents a novel approach for detection and location of power quality disturbances based on wavelet transform and artificial neural network. The wavelet transform is the projection of a discrete signal into two spaces: the approximation space and a series of detail spaces. The implementation of the projection operation is done by discrete-time subband decomposition of input signals using filtering followed by downsampling. The wavelet transform is utilized to produce representative feature vectors that can accurately capture the characteristics of power quality disturbance, exploring feature extraction of disturbance signal to obtain dynamic parameters. The feature vector obtained from wavelet decomposition coefficients are utilized as input variables of neural network for pattern classification of power quality disturbances. The training algorithm shows great potential for automatic power quality monitoring technique with on-line detection and classification capabilities. The combination performance of wavelet transform with neural network is evaluated by simulation results, approving that the proposed method is effective for analysis of power quality signal.