An improved adaptive filtering approach for power quality analysis of time-varying waveforms. (January 2019)

• Record Type: Journal Article
• Authors: Thirumala, Karthik; Umarikar, Amod C.; Jain, Trapti
• Is Part Of: Measurement. Volume 131(2019)
• Page Start: 677
• Page End: 685
• Publication Date: 2019-01
• Subjects: Empirical wavelet transform (EWT) -- Fast Fourier transform (FFT) -- Hilbert transform (HT) -- Power quality (PQ); Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.08.073
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A new adaptive time-frequency technique is proposed for power quality (PQ) analysis. Proposed approach aims for fast and better segmentation of the Fourier spectrum. The mono-components extracted are accurate and help in recognizing PQ disturbances. Feasible for real time monitoring of voltage and current signals. Abstract: An improved adaptive technique is proposed to estimate the frequencies, thereby decomposing the nonstationary signal for the analysis of time-varying disturbances. The proposed method is based on the successive application of FFT and followed by the adaptive filter design. The FFT based frequency estimation has been performed based on divide to conquer principle for accurate estimation of harmonics and inter-harmonics. The estimated frequencies are processed and the empirical wavelet filters are designed accordingly for extracting the mono-frequency components. Thereafter, the instantaneous frequency and amplitude of each component have been estimated by applying Hilbert transform. The effectiveness of the proposed method has been verified by testing it on various simulated as well as measured power quality signals containing harmonics, inter-harmonics, fundamental frequency deviation, transients, sag and swell. The results confirm that the proposed method is able to analyse the time-varying power quality signals accurately and rapidly, making it suitable for real-time monitoring.