Detection and classification of power quality event using wavelet transform and weighted extreme learning machine

The objective of this paper is to detect the Power Quality Events (PQEs) by Wavelet Transform (WT) and classification by Weighted Extreme Learning Machine (WELM). Power Quality Events are non-stationary in nature and Discrete Wavelet Transform (DWT) is used to analyze those signal by Multi Resolution Analysis (MRA). In this approach, the distinctive features of PQ event signals have been acquired by applying the WT on all the spectral components and in order to analyze the performance of the proposed method on noisy conditions, three types of PQ event data sets are constructed by accumulating noise of 25, 35 and 45dB. Weighted ELM is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which is implemented to recognizing the various PQEs classes. Based on very high performance under ideal and noisy conditions, the proposed WT-WELM method has robust recognition structure that can be used in real power systems.