A Linearization of DFT Spectrum for Precision Power Measurement in Presence of Interharmonics

TL;DR

This paper presents a linearization algorithm based on DFT spectrum analysis for accurate power measurement in power systems with interharmonics, addressing asynchronous sampling issues and improving estimation accuracy and robustness.

Contribution

It introduces a novel linearization method that enhances power measurement precision in the presence of interharmonics and frequency shifts, outperforming existing techniques.

Findings

Reduces power estimation error by several times compared to existing methods.

Maintains computational efficiency with only 7 ms processing time for 10 power-line-cycles.

Demonstrates robustness under various noise and frequency deviation conditions.

Abstract

The presence of interharmonics in power systems can lead to asynchronous sampling, a phenomenon further aggravated by shifts in the fundamental frequency, which significantly degrades the accuracy of power measurements. Under such asynchronous conditions, interharmonics lose orthogonality with the fundamental and harmonic components, giving rise to additional power components. To address these challenges, this paper introduces a linearization algorithm based on DFT spectrum analysis for precise power measurement in systems containing interharmonics. The proposed approach constructs a system of linear equations from the DFT spectrum and solves it through efficient matrix operations, enabling accurate extraction of interharmonic components near the fundamental and harmonic frequencies (with a frequency interval $\geq$1 Hz). This allows for precise measurement of power across the fundamental, harmonic, interharmonic, and cross-power bands, as well as total power. Test results demonstrate that the proposed method accurately computes various power components under diverse conditions--including varying interharmonic/fundamental/harmonic intervals, fundamental frequency deviations, and noise. Compared to existing methods such as fast Fourier transform (FFT), Windowed interpolation FFT, and Matrix pencil-Singular value decomposition, the proposed technique reduces estimation error by several times to multiple folds and exhibits improved robustness, while maintaining a computational time of only 7 ms for processing 10-power-line-cycle (200 ms) data.