Wavelet based detection of power quality disturbance - A case study

This paper presents an effective method for Detection and classification of power quality disturbances using wavelet features. Wavelet transform is one of the signal processing technique which has the ability to analyze these power quality problems simultaneously in both time and frequency domain. The energy signatures extracted from wavelet coefficients are used to detect and localize the disturbances from the recorded voltage waveforms. The disturbances of interest include sag, swell, Flicker and Harmonics. Results of simulation and analysis demonstrate that the proposed method can achieve higher correct identification rate, compared to other methods. Different wavelet features like standard deviation and entropy method are used to classify and their classification accuracy is compared in this paper. A case study based on the measurement of voltage and current signal at Electrical and Electronics Department of Thiagarajar College of Engineering, Madurai was studied using the proposed wavelet approach.