Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

Different power system applications based on synchrophasors measured in different nodes of the electric grid require information about the statistical distribution of the errors introduced by the phasor measurement units (PMUs). The performance of these applications can be significantly affected by possible incorrect assumptions. The Gaussian distribution has been historically assumed in most of the approaches, but some more recent studies suggest the possibility of considering different distributions for more accurate modeling of the actual situation. In this article, proper statistical tools applied to the results achieved through a high-performance experimental test system are proposed to assess the statistical distribution of PMU errors under controlled steady-state conditions, thus providing a basis for defining suitable models to be used in specific applications

Space Vector Taylor–Fourier Models for Synchrophasor, Frequency, and ROCOF Measurements in Three-Phase Systems

Taylor-Fourier (TF) filters represent a powerful tool to design phasor measurement unit (PMU) algorithms able to estimate synchrophasor, frequency, and rate of change of frequency (ROCOF). The resulting techniques are based on dynamic representations of the synchrophasor, and hence, they are particularly suitable to track the evolution of its parameters during time-varying conditions. Electrical quantities in power systems are typically three-phase and weakly unbalanced, but most PMU measurement techniques are developed by considering them as a set of three single-phase signals; on the contrary, this peculiarity can be favorably exploited to improve accuracy and reduce the computational cost. In this respect, this paper proposes to directly perform the TF expansion of the space vector (SV) samples obtained from three-phase measurements. A new paradigm allows to independently estimate positive and negative sequence synchrophasors along with system frequency and ROCOF, leveraging the three-phase characteristics. The performance of the proposed technique is assessed by using test signals inspired by the standard IEEE C37.118.1-2011, including noise as well as magnitude and phase unbalance. Achieved results highlight the flexibility of the enhanced SV-based approach, which is capable to combine excellent dynamic performance together with an accurate estimation of both positive and negative sequence components

Algorithms for the synchrophasor measurement in steady-state and dynamic conditions

Phasor measurement units (PMUs) are becoming one of the key issues of power network monitoring. They have to be able to perform accurate estimations of current and voltage signals either under steady-state or dynamic conditions. The first part of this PhD thesis analyses the impact of the phasor models on the estimation accuracy, focuses on algorithms proposed in the literature for the estimation of phasors and studies their performance under several different conditions. On the basis of the results of this analysis, in the second part of this thesis an innovative approach to improve the performance of synchrophasor estimation is presented. The method proposes a modified version of the synchrophasor estimation algorithm which uses the non-orthogonal transform defined as Taylor-Fourier Transform (TFT) and which is based on a Weighted Least Squares (WLS) estimation of the parameters of a second order Taylor model of the phasor. The aim of the proposed enhancements is to improve the performance of the algorithm in presence of fast transient events and to achieve a Phasor Measurement Unit that is simultaneously compliant with both M and P compliance classes, suggested by the synchrophasor standard IEEE C37.118.1. In particular, while the TFT based adaptive algorithm is used for synchrophasor estimation, frequency and Rate of Change of Frequency (ROCOF) are estimated using the higher derivatives outputs of the adaptive TFT. Frequency estimation feedback is used to tune the algorithm and achieve better performance in off-nominal conditions. The proposed approaches are validated by means of simulations in all the static and dynamic conditions defined in the standard. In the last chapter, the algorithm proposed above is used in a novel architecture, compliant to IEC 61850, for a distributed IED-based PMU, to be used in electrical substations. In particular, a measurement architecture based on process bus and sampled values synchronized with IEEE 1588-2008 is proposed, so that voltage and current signals are acquired by a Merging Unit device, while the PMU signal processing is performed on a IED (Intelligent Electronic Device), in compliance with IEEE C37.118.1-2011

PMU-based distribution system state estimation with adaptive accuracy exploiting local decision metrics and IoT paradigm

A novel adaptive distribution system state estimation (DSSE) solution is presented and discussed, which relies on distributed decision points and exploits the Cloud-based Internet of Things (IoT) paradigm. Up to now, DSSE procedures have been using fixed settings regardless of the actual values of measurement accuracy, which is instead affected by the actual operating conditions of the network. The proposed DSSE is innovative with respect to previous literature, because it is adaptive in the use of updated accuracies for the measurement devices. The information used in the estimation process along with the rate of the execution are updated, depending on the indications of appropriate local metrics aimed at detecting possible variations in the operating conditions of the distribution network. Specifically, the variations and the trend of variation of the rms voltage values obtained by phasor measurement units (PMUs) are used to trigger changes in the DSSE. In case dynamics are detected, the measurement data are sent to the DSSE at higher rates and the estimation process runs consequently, updating the accuracy values to be considered in the estimation. The proposed system relies on a Cloud-based IoT platform, which has been designed to incorporate heterogeneous measurement devices, such as PMUs and smart meters. The results obtained on a 13-bus system demonstrate the validity of the proposed methodology that is efficient both in the estimation process and in the use of the communication resources

Line Impedance Estimation Based on Synchrophasor Measurements for Power Distribution Systems

Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible

Effect of Unbalance on Positive-Sequence Synchrophasor, Frequency and ROCOF Estimations

Phasor measurement units (PMUs) are the measurement devices fostering the transformation of electric power networks towards the smart grid paradigm. They should accurately measure synchrophasors, frequency, and rate of change of frequency (ROCOF), so that the management and control applications relying on PMU-based distributed monitoring systems can operate effectively. Commercial PMUs performance is typically guaranteed by the compliance with the IEEE standard C37.118.1, which is focused on PMUs for power transmission systems and defines testing conditions and error limits. However, actual operating conditions are much more variable than those covered by the standard, especially when PMUs are used in distribution networks. In particular, the standard does not consider unbalance, which may be negligible neither in transmission nor in distribution grids. For the first time, this paper analyzes the impact of unbalance on the accuracy of four of the most significant classes of signal processing algorithms for PMU measurements. Synchrophasor, frequency, and ROCOF estimation performances under different unbalance conditions are investigated in the test cases suggested by the IEEE C37.242-2013 guide. Novel analytic expressions to predict the errors are derived and validated, and they are proved to be useful for an effective implementation of PMU algorithms intended for both distribution and transmission systems

Results of five years monitoring for Toxoplasma gondii infection in animals by the official Italian Zoonoses Informative System (SINZOO)

In order to drive effective public health preventive measures for human toxoplasmosis a scrupulous epidemiological monitoring of animal toxoplasmosis is essential. BACKGROUND AND AIM:T. gondii is the fourth most important parasite in the world and second out of 14 foodborne pathogens in the USA and in Europe. Meat-borne transmission of T. gondii causes most of food-borne infections in Europe (EFSA-European Food Safety Authority). SINZOO is part of the Veterinary Informative System of the Italian Ministry of Health. It collects and transmit data to EFSA, published in the annual EFSA/ECDC summary reports on zoonoses. The aim of this study was to evaluate the effectiveness of SINZOO for epidemiological surveillance of toxoplasmosis in Italy. METHODS:Among animal species tested in Italy between 2015 and 2019 the ones most commonly reared for human consumption (sheep, cattle, pig, goats) were selected, moreover wild boars, wild ruminants, cats. RESULTS:Infection rates ranged from 0.73% in wild boars to 45.72% in sheep. Total number of tested animals ranged from 37 pigs in 2015 to 3449 sheep in 2018. Besides a relevant incidence among wild boars in 2018 (45%) and 2019 (32%), higher infection rates were more often reported among sheep and pigs. Between 2018 and 2019 67% of the overall analyses were carried out in one region (Sardinia), mostly on one species (sheep) and emerged from targeted research or clinical investigation. In fact in 2019 83.45% of analyses were performed following clinical suspicions while only 8.43% came from official controls, highlighting toxoplasmosis underestimation by the national veterinary health system. CONCLUSIONS:Despite EFSA recognizes the relevance of toxoplasmosis, this is not included among zoonoses under mandatory notification, making animal epidemiological surveillance rather scarce and uneven. Data reported to SINZOO suggest that T. gondii is still a relevant hazard to monitor by meat inspection and in-farm survey, for effective epidemiological evaluations and appropriate public health interventions. This issue characterizes Italy and Europe, highlighting that toxoplasmosis monitoring should be made mandatory and with uniform rules