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Application of an empirical wavelet transform (EWT)-based time-frequency technique is proposed in this paper for the estimation of power-quality indices (PQIs). This technique first estimates the frequency components present in the distorted signal, computes the boundaries, and then filtering is done based on the boundaries computed. This technique is effective and offers many advantages over other techniques because the scaling function and wavelets adapt themselves according to the information contained in the signal and no prior information regarding the signal is required. Besides this, the advantages of the discrete wavelet transform (DWT), wavelet packet transform (WPT), short-time Fourier transform (STFT), windowing techniques, and filter bank techniques also hold. Simulated and experimental test signals with power-quality disturbances have been analyzed and the effectiveness of this technique has been shown by comparing the PQIs estimated by using the proposed method with those obtained using the DWT and WPT. The results confirm that the EWT efficiently extracts the mono component signals from the actual distorted signal and thereby accurately estimates the PQIs.
Thirumala et al. (Tue,) studied this question.