Improved wavelet based detection of power quality events in the presence of noise

Due to nonlinear loads and other reasons, the power quality gets deteriorated and is of a major significance in recent times. In order to mitigate the problems due to power quality events such as sag, swell, interrupt, etc., good estimation of the parameters of the power quality events is required. Invariably, the power system waveforms get corrupted due to noise also. The estimation of the parameters and the removal of noise requires a time-frequency analysis. The discrete wavelet transform is highly suitable for time-frequency analysis due to its suitability for the analysis of nonstationary signals. In this paper, a method is proposed for the effective removal of the noise and the estimation of the parameters of the power quality events. Simulation results confirm the effectiveness of the proposed approach.