Detection and classification of power quality events using empirical wavelet transform and error minimised extreme learning machine

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The main purpose of this paper is to detect the power quality events (PQEs) by empirical wavelet transform (EWT) and classify by error minimised extreme learning machine (EMELM). Empirical wavelet transform (EWT) is used to analyse the non-stationary power quality event signals by multi-resolution analysis (MRA). Here, the disturbance energy index feature vector of different electric power supply signals have been acquired by applying the EWT on all the spectral components and to analyse the overall efficiency of the proposed method on both ideal and noisy environments, three types of PQ event data sets are constructed by accumulating the noise of 25, 35 and 45 dB. Extreme learning machine (ELM) is an advanced and efficient classifier, which is implemented to recognise the single as well as multiple PQ fault classes. Based on very high performance under ideal and noisy environment, the new EWT-EMELM method can be implemented in real electrical power systems. The feasibility of proposed method is tested by simulation to verify its cogency.