An enhanced interpolated-modulated sliding DFT for high reporting rate PMUs

The field of application of Phasor Measurement Units (PMUs) might be limited by the PMU measurements reporting latencies and achievable reporting rates, particularly with respect to power system protection applications that typically require very low latencies. A way to speed-up synchrophasor estimation algorithms based on the use of the Discrete Fourier Transform (DFT) refers to the usage of stable and accurate recursive processes for the DFT estimation. In this respect, this paper presents a synchrophasor estimation algorithm, called Interpolated-Modulated Sliding DFT (IpMSDFT), characterized by high accuracies and reduced latencies, enabling reporting rates up to thousands of synchrophasor per second. It is composed by two stages: (i) a guaranteed-stable technique for sample-by-sample DFT computation; (ii) an enhanced version of the classical IpDFT algorithm for synchrophasor estimation. The algorithm is analytically formulated and its digital design tailored to allow a feasible deployment on an FPGA-based PMU. The IpMSDFT-based PMU is finally validated with respect to the numerical stability of the proposed solution, its reporting latencies and the achievable reporting rates

Iterative-Interpolated DFT for Synchrophasor Estimation: A Single Algorithm for P- and M-Class Compliant PMUs

We present a single synchrophasor estimation (SE) algorithm that is simultaneously compliant with both P and M phasor measurement unit (PMU) performance classes. The method, called iterative-interpolated discrete Fourier transform (i-IpDFT), iteratively estimates and compensates the effects of the spectral interference produced by both a generic interfering tone, harmonic or interharmonic, and the negative image of the fundamental tone. We define the three-point i-IpDFT technique for cosine and Hanning window functions and we propose a procedure to select the i-IpDFT parameters. We assess the performance of the i-IpDFT with respect to all the operating conditions defined in the IEEE Std. C37.118 for P- and M-class PMUs. We demonstrate that the proposed SE method is simultaneously compliant with all the accuracy requirements of both PMU performance classes

Compressive Sensing Applications in Measurement: Theoretical issues, algorithm characterization and implementation

At its core, signal acquisition is concerned with efficient algorithms and protocols capable to capture and encode the signal information content. For over five decades, the indisputable theoretical benchmark has been represented by the wellknown Shannon’s sampling theorem, and the corresponding notion of information has been indissolubly related to signal spectral bandwidth. The contemporary society is founded on almost instantaneous exchange of information, which is mainly conveyed in a digital format. Accordingly, modern communication devices are expected to cope with huge amounts of data, in a typical sequence of steps which comprise acquisition, processing and storage. Despite the continual technological progress, the conventional acquisition protocol has come under mounting pressure and requires a computational effort not related to the actual signal information content. In recent years, a novel sensing paradigm, also known as Compressive Sensing, briefly CS, is quickly spreading among several branches of Information Theory. It relies on two main principles: signal sparsity and incoherent sampling, and employs them to acquire the signal directly in a condensed form. The sampling rate is related to signal information rate, rather than to signal spectral bandwidth. Given a sparse signal, its information content can be recovered even fromwhat could appear to be an incomplete set of measurements, at the expense of a greater computational effort at reconstruction stage. My Ph.D. thesis builds on the field of Compressive Sensing and illustrates how sparsity and incoherence properties can be exploited to design efficient sensing strategies, or to intimately understand the sources of uncertainty that affect measurements. The research activity has dealtwith both theoretical and practical issues, inferred frommeasurement application contexts, ranging fromradio frequency communications to synchrophasor estimation and neurological activity investigation. The thesis is organised in four chapters whose key contributions include: • definition of a general mathematical model for sparse signal acquisition systems, with particular focus on sparsity and incoherence implications; • characterization of the main algorithmic families for recovering sparse signals from reduced set of measurements, with particular focus on the impact of additive noise; • implementation and experimental validation of a CS-based algorithmfor providing accurate preliminary information and suitably preprocessed data for a vector signal analyser or a cognitive radio application; • design and characterization of a CS-based super-resolution technique for spectral analysis in the discrete Fourier transform(DFT) domain; • definition of an overcomplete dictionary which explicitly account for spectral leakage effect; • insight into the so-called off-the-grid estimation approach, by properly combining CS-based super-resolution and DFT coefficients polar interpolation; • exploration and analysis of sparsity implications in quasi-stationary operative conditions, emphasizing the importance of time-varying sparse signal models; • definition of an enhanced spectral content model for spectral analysis applications in dynamic conditions by means of Taylor-Fourier transform (TFT) approaches

Controllo distribuito ed in retroazione della potenza reattiva per la regolazione di tensione e la minimizzazione delle perdite. Distributed reactive power feedback control for voltage regulation and loss minimization

Sfruttando i microgeneratori dislocati in una smart grid si forniscono iniezioni di potenza reattiva con l'obiettivo di controllare le tensioni nodali entro un intervallo di tolleranza. Si esaminano alcuni algoritmi presenti in letteratura e viene proposta una strategia di controllo finalizzata in primo luogo alla voltage regulation ed in secondo luogo alla minimizzazione delle perdite di potenza. Le prestazioni degli algoritmi descritti sono analizzate attraverso simulazioni in matla