Abstract
The most recent estimation algorithms for Phasor Measurement Units (PMUs) rely on increasingly complex signal models. The rationale of this general approach is that such model can support algorithms able to estimate not only amplitude, phase, frequency and rate of change of frequency(ROCOF) of the waveform fundamental component, but also the parameters of possible disturbances, thus reducing their impact on measurement accuracy. Unfortunately, as model complexity grows, the sensitivity to stationary wideband noise and narrowband disturbances grows as well, particularly over short observation intervals. While using short data records improves responsiveness and reduces the computational burden, harmonics and interharmonic components become particularly harmful for synchrophasor, frequency and ROCOF estimation. To tackle this problem, in this paper a novel decorrelation-based approach is proposed. The basic idea is to preprocess a collected data record through a tailored, linear transformation replacing harmonics and interharmonics with wideband noise, prior to running an estimation algorithm. Several simulation results in different operating conditions confirm the effectiveness of the proposed approach over short observation intervals and pave the way to new decorrelation-based measurement techniques for PMUs