Efficient detection for multifrequency dynamic phasor analysis

Analysis of harmonic and interharmonic phasors is a promising smart grid measurement and diagnostic tool. This creates the need to deal with multiple phasor components having different amplitudes, including interharmonics with unknown frequency locations. The Compressive Sensing Taylor-Fourier Multifrequency (CSTFM) algorithm provides very accurate results under demanding test conditions, but is computationally demanding. In this paper we present a novel frequency search criterion with significantly improved effectiveness, resulting in a very efficient revised CSTFM algorithm

PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications

In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection and Control (WAMPAC) applications. However, a standard approach towards ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and inter-harmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model looses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme and we show their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE Transactions on Instrumentation and Measurement (acceptance date: 9 March 2019

Measuring Cerebral Activation From fNIRS Signals: An Approach Based on Compressive Sensing and Taylor-Fourier Model

Functional near-infrared spectroscopy (fNIRS) is a noninvasive and portable neuroimaging technique that uses NIR light to monitor cerebral activity by the so-called haemodynamic responses (HRs). The measurement is challenging because of the presence of severe physiological noise, such as respiratory and vasomotor waves. In this paper, a novel technique for fNIRS signal denoising and HR estimation is described. The method relies on a joint application of compressed sensing theory principles and Taylor-Fourier modeling of nonstationary spectral components. It operates in the frequency domain and models physiological noise as a linear combination of sinusoidal tones, characterized in terms of frequency, amplitude, and initial phase. Algorithm performance is assessed over both synthetic and experimental data sets, and compared with that of two reference techniques from fNIRS literature

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

Impact of Estimation Uncertainty in PMU-Based Resynchronization of Continental Europe Synchronous Areas

Power system stability is a task that every system operator (SO) is required to achieve daily to ensure an uninterruptible power supply. Especially at the transmission level, for each SO it is of utmost importance to ensure proper exchange of information with other SOs, mainly in case of contingencies. However, in the last years, two major events led to the splitting of Continental Europe into two synchronous areas. These events were caused by anomalous conditions which involved in one case the fault of a transmission line and in the other a fire outage in proximity to high-voltage lines. This work analyzes these two events from the measurement point of view. In particular, we discuss the possible impact of estimation uncertainty on control decisions based on measurements of instantaneous frequency. For this purpose, we simulate five different configurations of phasor measurement units (PMUs), as characterized by different signal models, processing routines, and estimation accuracy in the presence of off-nominal or dynamic conditions. The objective is to establish the accuracy of the frequency estimates in transient conditions, more specifically during the resynchronization of the Continental Europe area. Based on this knowledge, it is possible to set more suitable conditions for resynchronization operations: the idea is to consider not only the frequency deviation between the two areas but also to take into account the respective measurement uncertainty. As confirmed by the analysis of the two real-world scenarios, such an approach would allow for minimizing the probability of adverse or even dangerous conditions such as dampened oscillations and inter-modulations

Design of Compressive Sensing Adaptive Taylor-Fourier Comb Filters for Harmonic Synchrophasor Estimation

In modern power systems, phasor measurements are expected to deal with challenging conditions, e.g., fast dynamics and high distortion levels. Taylor-Fourier Multifrequency models represent a promising solution, but their performance is strongly related to the accurate extraction of the signal spectral support. In this context, this paper proposes an enhanced method for support recovery that exploits the inherent block-sparsity properties of electrical signals. The proposed method is fully characterized in diverse and distorted test conditions, inspired by reference standards and real-world scenarios. The comparison against another Compressive Sensing based approach confirms the significant improvement in terms of both recovered support exactness and synchrophasor measurement accuracy

Impact of Time Dissemination Technologies on Synchrophasor Estimation Accuracy

The paper discusses the influence of the time synchronization technique on the synchrophasor estimation accuracy in modern Phasor Measurement Units (PMU). Specifically, we make reference to Global Positioning System (GPS), Precision Time Protocol (PTP) and White Rabbit time disseminations. The paper presents a PMU integrating the three technologies, and assess its performance in steady state conditions. The experimental validation demonstrates that the more deterministic the time source, the better the performance in terms of phase estimation accuracy