Guglielmo Frigo

Resolution Enhancement by Compressive Sensing in Power Quality and Phasor Measurement

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.

Compressive Sensing of a Taylor-Fourier Multifrequency Model for Synchrophasor Estimation

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.

Definition of Accurate Reference Synchrophasors for Static and Dynamic Characterization of PMUs

The calibration of phasor measurement units (PMUs) consists of comparing coordinated universal time time-aligned phasors (synchrophasors) measured by the PMU under test, against reference synchrophasors generated through a PMU calibrator. The IEEE Standard C37.118–2011 and its latest amendment (IEEE Std) describe compliance tests for static and dynamic conditions, and indicate the relative limits in terms of accuracy. In this context, this paper focuses on the definition and accuracy assessment of the reference synchrophasors in the test conditions defined by the above IEEE Std. In the first part of this paper, we describe the characterization of a nonlinear least-squares fitting algorithm used to determine the parameters of these reference synchrophasors. For this analysis, we deploy the proposed algorithm in a PMU calibrator and characterize the algorithm performance within the actual hardware implementation for both static and dynamic test conditions. More specifically, we generate reference waveforms through a highly stable high-resolution digital-to-analog converter and evaluate how the algorithm parameters (observation interval length and sampling frequency) affect the solution accuracy. In the second part, we discuss on the appropriateness of the synchrophasor model in the evaluation of PMU performance under step test conditions. In this regard, we propose an alternative time-domain approach to assess the synchrophasor estimate during transient events.

A smartphone-based indoor localization system for visually impaired people

The World Health Organization assesses the number of visually impaired people to be nearly 285 million in August 2014, of whom 39 million are blind. One of the most important discomfort factors for these persons is known to be the difficulty in moving and orienting by themselves in unfamiliar surroundings. Nowadays, several devices are currently available for supporting these persons in there everyday life. To this end, the attention in this paper is mainly focused on the localization and navigation in indoor environment. The solution adopted in this paper consists in populating a database of virtual maps, that the user itself contributes to create by exploring the surrounding environment. An inertial platform, represented by a set of sensors (basically accelerometer, gyroscope, electronic compass) placed in a device worn by user, is used at the purpose as sensing system. This approach does not require the installation of external equipments since it relies on a smartphone, which is used both as measurement platform and user interface, and, in particular, does not require any a-priori knowledge of the indoor environment. The application will be described in the paper where some experimental preliminary results will also be discussed.

Definition and assessment of reference values for PMU calibration in static and transient conditions

The calibration of Phasor Measurement Units (PMUs) consists of comparing Coordinated Universal Time (UTC) time-stamped phasors (synchrophasors) estimated by the PMU under test, against reference synchrophasors generated through a PMU calibrator. The IEEE Standard C37.118-2011 and its amendment (IEEE Std) describe compliance tests for static and dynamic conditions, and indicate the relative limits in terms of accuracy. In this context, the paper focuses on the definition and accuracy assessment of the reference synchrophasors in the test conditions dictated by the above IEEE Std. In the first part of the paper, we describe the characterization of a nonlinear least-squares (NL-LSQ) fitting algorithm used to determine the parameters of the reference synchrophasors. We analyse the uniqueness and robustness of the solution provided by the algorithm. We assess its accuracy within the whole range of static tests required by the IEEE Std. In the second part, we discuss the appropriateness of synchrophasor model to evaluate the PMU performance in step test conditions. We compare the proposed algorithm against two synchrophasor estimation algorithms. Finally, we propose a time domain process for the better evaluation of PMU performances in transient conditions.

Compressive sensing plus Taylor-Fourier Transform for synchrophasor estimation

The synchrophasor Standard IEEE C37.118.1, along with its amendment, defines two compliance classes for phasor measurement units (PMUs), the different test signals to be adopted for verification and the limits that should be respected for each test condition. In recent years, many synchrophasor estimation algorithms have been proposed to deal with the operative conditions identified by the standard, that can be both steady state and dynamic. The research has shown that some disturbances, such as interharmonic interfering signals, can seriously degrade synchrophasor measurement accuracy. In this work, a new two-stage approach, relying on Compressive Sensing (CS) and Taylor Fourier Transform (TFT), is proposed to identify the most relevant interfering components in the signal spectrum and to limit their impact on synchrophasor estimation. Simulation results are reported to confirm the algorithm efficiency.

EEG gradient artifact removal by compressive sensing and Taylor-Fourier transform

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represents a powerful tool for brain activity investigation. Unfortunately, EEG data collected during concurrent fMRI are affected by very large artifacts. This paper focuses on the gradient artifact (GRA), related to the sawtooth profiles of magnetic flux inside the MRI scanner. A novel removal algorithm is proposed and validated on both simulation and experimental data. A super-resolution method, based on compressive sensing, determines GRA harmonic frequencies. Amplitudes and phases of GRA components are estimated by means of the Taylor-Fourier transform (TFT), complying with dynamic operating conditions. Unlike averaging techniques, well-known in the literature, this allows computation of a specific template for each artifact occurrence, which is subtracted from the original data. Experimental results show a significant reduction of spurious components in all the considered conditions. No significant distortions are introduced in spectral power distribution, allowing reliable clinical interpretation of the acquired trace.

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.

Frequency tracking for efficient phasor measurement based on a CSTFM model

The paper presents a technique based on the use of single-variable Kalman filters (KFs) to track the frequency variation of signal components in multifrequency phasor analysis. KF-based tracking is employed for accurate frequency estimation of both harmonic and inter-harmonic components in a Compressive Sensing Taylor Fourier Multifrequency (CSTFM) algorithm. This novel approach improves robustness of the CSTFM method to the effects of spectral interference among harmonic and interharmonic components, allowing better estimates of each component and extending the range of application beyond pure phasor measurement unit (PMU) devices. Computational efficiency compared to a plain CSTFM algorithm is also enhanced. Significant case studies, with signals including timevarying harmonic and interharmonic components, are analyzed and discussed with regards, in particular, to frequency estimation. Moreover, it is shown that intermittent components can be handled without loss of accuracy by KF-based tracking features.

A compressive sensing spectral model for fNIRS haemodynamic response de-noising

In the biomedical scenario, near-infrared spectroscopy (NIRS) is employed as a non-invasive brain imaging technique. In particular, functional near-infrared spectroscopy (fNIRS) measures the brain response, also known as haemodynamic response (HR), to pre-defined stimuli. Processing of fNIRS data requires a great effort to extrapolate the informative component from a noisy mixture of physiological and spurious contributions. In this paper a novel fNIRS de-noising algorithm is presented and validated over both synthetic ideal and synthetic realistic data. The short-separation channel signal is divided into nonoverlapping short sequences. For each of them, a specific noise model is identified and subtracted from the corresponding standard channel data. The algorithm relies on a combination of a super-resolution technique based on Compressive Sensing theory and spectral analysis performed via Taylor-Fourier transform. Preliminary experimental results show a significant reduction of spurious components in all the considered conditions. No significant distortions are introduced in the recovered HR, ensuring reliable clinical interpretation of the acquired trace.