Compressive Sensing Empirical Wavelet Transform for Frequency-Banded Power Measurement Considering Interharmonics

TL;DR

This paper introduces a novel Compressive Sensing Empirical Wavelet Transform method that enhances frequency resolution and measurement accuracy of power components, including interharmonics, in power systems.

Contribution

The paper proposes integrating compressive sensing with EWT to improve spectral resolution and measurement accuracy for interharmonics and harmonics in power measurement.

Findings

Significantly improves measurement precision under noisy conditions

Enhances frequency resolution beyond traditional methods

Effective in dynamic power measurement scenarios

Abstract

Power measurement algorithms based on Fourier transform are susceptible to errors caused by interharmonics, while wavelet transform algorithms are particularly sensitive to even harmonics due to band decomposition effects. The empirical wavelet transform (EWT) has been demonstrated to improve measurement accuracy by effectively partitioning transition bands. However, for detecting interharmonic components, the limitation of the observation time window restricts spectral resolution, thereby limiting measurement accuracy. To address this challenge, this paper proposes a Compressive Sensing Empirical Wavelet Transform (CSEWT). The approach aims to enhance frequency resolution by integrating compressive sensing with the EWT, allowing precise identification of components across different frequency bands. This enables accurate determination of the power associated with the fundamental frequency, harmonics, and interharmonics. Test results indicate that the proposed CSEWT method can significantly improve the precision of individual frequency component measurements, even under dynamic and noisy conditions.