Adaptive spectral trend based optimized EWT for monitoring the parameters of multiple power quality disturbances

Abstract

The growing penetration of renewable energy sources leads to power quality disturbances (PQDs) being multiple. The real-time monitoring of the parameters of multiple power quality disturbances (MPQDs) can help electricity distributors better control power quality problems. In this paper, an adaptive spectral trend-based optimized empirical wavelet transform (EWT) is proposed to analyze MPQDs. First, the upper and lower envelopes of the signal frequency spectrum are fitted by piecewise cubic Hermite interpolation. Then the sum of the upper and lower envelopes is halved as a spectral trend. These steps are repeated until all peak-to-peak distances are greater than the key threshold, indicating that the spectral trend has already been optimized. Third, main frequency components are calculated based on peaks of the optimal spectral trend, which can be used as a basis for spectrum segmentation. Eventually, the components decomposed by EWT represent different disturbance elements, whose parameters can be further calculated by Hilbert transform (HT). To verify the effectiveness of the proposed algorithm, two databases are applied for analysis: synthetic signals, and recording signals by the experimental platform. At the same time, four advanced algorithms are used for comparison, which are scale-space optimized EWT (SOEWT), improved EWT (IEWT), variational mode decomposition (VMD) and fast Stockwell transform (FST). The results showed that the proposed method can accurately segment the spectrum and prevent the inappropriate segmentation of the original EWT so that it can monitor the disturbance parameters with high precision. In addition, the proposed algorithm has a simple computation.