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In modern power systems, the integration of renewable energy sources relies on dedicated inverters whose power electronic circuitry switches at high frequencies and causes conducted emissions in the supraharmonic range, i.e., from 9 to 150 kHz. In this regard, the normative framework is still lacking a reference measurement method as well as a set of emission limits and performance requirements. From a metrological point of view, it is important to evaluate whether some of the power quality indices adopted for radiated emissions could be transposed also in this context. In particular, the paper considers a recent algorithm for the identification of supraharmonic components and discusses how its estimates affect the estimation of quasi-peak values. To this end, the paper describes the implementation of a fully digital approach and validates the results by means of an experimental comparison against a traditional quasi-peak detector. The proposed analysis confirms the potential of the considered approach and provides some interesting insights about the reliability of quasi-peak estimation in supraharmonic range. [ABSTRACT FROM AUTHOR]

With the expanding role of converter-interfaced distributed energy resources, modern power grids are evolving towards low-inertia networks that are increasingly vulnerable to extreme dynamics. Consequently, advanced signal processing techniques are needed to accurately characterize measured signals in power systems during non-stationary conditions. However, as advocated by recent literature, state-of-the-art phasor estimation methods are unable to sufficiently capture the broadband nature of these signal dynamics since they rely on a quasi-steady state, single tone model. Inspired by previous work by the authors, this paper proposes a signal processing method that uses a dictionary of kernels, modeling common signal dynamics, to compress time-domain information into a few coefficients. The identified signal model and the extracted coefficients capture the broadband spectrum of typical power system signal dynamics and allow for an improved reconstruction of the measured signal. [ABSTRACT FROM AUTHOR]

In spectrum sensing and wireless communications analysis, signals of interest typically occupy only a few among several possible bands and do so for short-time bursts within a given observation interval. The frequency and time location of these signals may be known only approximately a priori (for instance, the nominal frequency of a wireless channel) or, in general, not accurately enough to set up more detailed measurements. In this paper, a compressive sensing (CS) algorithm is employed to provide accurate preliminary information and suitably preprocessed data for a vector signal analysis algorithm. The CS paradigm exploits sparsity, a feature common to several signals of interest, to allow the design of efficient data acquisition schemes. It is shown that the application of the sensing method called modulated wideband converter allows one to successfully extract specific signal bursts from a record of samples covering a longer time interval and a broader bandwidth. The accuracy of the extraction process is analyzed and the results referring to vector analysis are presented. This provides blind spectrum sensing and signal extraction capabilities that can effectively simplify the time-consuming process of setting up a spectrum analyzer for vector signal analysis. [ABSTRACT FROM PUBLISHER]

Synchrophasor measurements, performed by phasor measurement units (PMUs), are becoming increasingly important for power system network monitoring. Synchrophasor standards define test signals for verification of PMU compliance, and set acceptance limits in each test condition for two performance classes ( $P$ and $M$ ). Several PMU algorithms have been proposed to deal with steady-state and dynamic operating conditions identified by the standard. Research and discussion arising from design, implementation, testing and characterization of PMUs evidenced that some disturbances, such as interharmonic interfering signals, can seriously degrade synchrophasor measurement accuracy. In this paper, a new compressive sensing (CS) approach is introduced and applied to synchrophasor measurements using a CS Taylor–Fourier (TF) multifrequency (CSTFM) model. The aim is to exploit, in a joint method, the properties of CS and the TF transform to identify the most relevant components of the signal, even under dynamic conditions, and to model them in the estimation procedure, thus limiting the impact of harmonic and interhamonic interferences. The CSTFM approach is verified using composite tests derived from the test conditions of the synchrophasor standard and simulation results are presented to show its potentialities. [ABSTRACT FROM PUBLISHER]

Measurement in power systems and, particularly, in smart grids and smart microgrids is often concerned with harmonic analysis of voltage and current waveforms. The use of Fourier-based algorithms is widespread, and the limits following from the fundamental time-versus-frequency tradeoff that relates observation time to frequency resolution are well understood. This paper presents the application of an algorithm based on the principles of compressive sensing that can achieve an order-of-magnitude resolution improvement without significantly extending total observation time. For harmonic analysis in power systems, this means that accurate results can be obtained using shorter observation intervals, which allow to effectively track changes and reduce the effect of transients on measurements. The application of the algorithm to harmonics and interharmonics, as well as to phasor measurement, is considered and analyzed. [ABSTRACT FROM PUBLISHER]

The evaluation of the signal frequency and Rate of Change of Frequency (RoCoF) from voltage or current waveforms is used for critical grid control, monitoring and protection applications. However, when step changes in the amplitude or phase of the signal occur, conventional frequency and RoCoF estimation methods, typically based on phasor models, are highly unreliable and can yield large frequency errors. To address this issue, this paper proposes a technique that uses dictionaries based on common signal dynamic models to identify and track amplitude and/or phase steps in AC signals. Additional signal dynamics including amplitude modulations, frequency ramps and harmonic tones are also characterized. Distinct from a previous iteration of this method developed by the authors, the proposed Step Change Detection (SCD) technique separates the envelope and angle of the signal's analytic form for independent analysis of these components. For numerical validation the method is applied to synthetically generated signals with challenging combinations of signal dynamics. The technique is shown to greatly improve frequency and RoCoF approximations as compared to state-of-the-art phasor estimation methods. • Phasor Measurement Units use narrowband models suited for stationary conditions. • Phasor-based frequency analysis performs poorly when signal dynamics are present. • Dictionaries of parameterized signal models can accurately detect step dynamics. • Characterization of step dynamics allows for improved RoCoF estimation. • Correct estimation of frequency and RoCoF is key to grid protection and monitoring. [ABSTRACT FROM AUTHOR]