Design and Performance Prediction of Space Vector-Based PMU Algorithms

Abstract

Phasor measurement units (PMUs) are expected to be the basis of modern power networks monitoring systems. They are conceived to allow measuring the phasor, frequency, and rate of change of frequency (ROCOF) of electrical signals in a synchronized way and with unprecedented accuracy. PMUs are intended to apply to three-phase systems and to track signal parameters evolution during network dynamics. For these reasons, the design of the algorithms and, in particular, of the filters that allow rejecting the disturbances while preserving the passband signal content is a paramount concern. In this paper, a novel space vector approach is proposed. It exploits the three-phase nature of the monitored signals together with proper lowpass and differentiation filters. Analytical formulas for performance prediction under almost all the test conditions prescribed by the synchrophasor standard C37.118.1 for PMUs, are introduced. The given expressions are extremely accurate, thus allowing to derive the filter design criteria for phasor, frequency, and ROCOF computation, so that the requirements in terms of estimation errors can be easily translated into filter specifications. The implications of the proposed approach in practical PMU design are illustrated by means of two simple design examples matching P and M compliance classes, respectively, for all the test cases of the standard. The reported performance proves the validity of the proposal

Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison

“…In this paper, we present a comparison among three algorithms for estimating positive sequence synchrophasor, frequency and ROCOF starting from a three-phase signal. The considered techniques are the SV filtering algorithm (SV-F) [24], the SV Taylor-Fourier algorithm (SV-TF) [26] and the SV IpDFT algorithm (SV-IpDFT) [25]. All of them feature high flexibility: their parameters can be tuned in order to meet specific requirements in terms of accuracy and latency according to the application.…”

“…Instead of dealing with each phase separately, the SV with respect to a reference frame rotating at the system nominal frequency is computed. Digital filters whose frequency responses can be customized to reach specific performance goals [24] are employed to extract the contribution of the positive sequence component from the complex-valued SV signal. It is worth noting that the SV approach can be also used in conjunction with other well-known methods for estimating synchrophasor, frequency and ROCOF such as interpolated DFT (IpDFT) [25] or Taylor-Fourier (TF) filtering [26], thus leveraging the three-phase symmetry of power systems for reduced computational cost and improved accuracy.…”

On the Importance of Characterizing Virtual PMUs for Hardware-in-the-Loop and Digital Twin Applications

“…It is worth remarking on Taylor-Kalman Filters (TKFs) [71] and Adaptive and Extended Kalman Filters (AKFs, EKFs) [72,73]. A small number of other approaches are based on different techniques such as Taylor Weighted Least-Squares (TWLS) [74], Space Vectors (SVs) [75], and wavelets [76]. Given the broadness of the topic enveloping several techniques, comparative studies between PMU estimation algorithms have also been presented in the literature.…”

Multi-Labeled Recognition of Distribution System Conditions by a Waveform Feature Learning Model

“…A classifier embedded in feeder devices with improved labeling and waveform modeling could acquire waveforms and recognize abnormal conditions [19,20]. In terms of waveform signal processing, the focuses are on properly extracting waveform signals [21,22], and on waveform signal filtering for exclusion of field waveform noises.…”