Detection and Classification of three phase Power Quality events using Wavelets Transforms and Soft Computing Techniques

Abstract

Analysis of power quality and its related problems is of very much important for both the utilities and end users. There are a large number of concerned authorities to monitor and mitigate the power quality problems. It requires a larger amount to deliver a poor power. So by considering the global economical losses, it is very much urgent to mitigate the various problems affecting the true power. Classification of problems is equally important to mitigation. The common power quality problems occurring are voltage sag, swell, harmonics, flickers etc. Good power determines the fitness of electric power to consumer devices and appliances. It is very important to maintain the detection accuracy of power quality events throughout the operation span. This paper deals with a study based on signal processing algorithms and soft computing techniques for the detection, classification and estimation of power quality events. The literature review points toward the application of wavelet transforms with different filters for achieving feature extraction. The power quality disturbance model is simulated using Simulink toolbox. It is observed that every power quality wavelet disturbance will show unique characteristics and it is generally used to provide an adoptable classification of power quality events. Because of the non-stationary and transitory behavior of the power quality events, the classification goes on challenging and demanding. Thus the feature extraction along with artificial neural network and fuzzy logic incorporated as a powerful tool