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BackgroundEvidence linking risk of lymphoma and B-cell lymphoma subtypes to ionizing radiation is inconclusive, particularly at low exposure levels.MethodsWe investigated risk of lymphoma (all subtypes), B-cell lymphomas, and its major subtypes, associated with low-level occupational exposure to ionizing radiation, in 2346 lymphoma cases and 2463 controls, who participated in the multicenter EpiLymph case-control study. We developed a job-exposure matrix to estimate exposure to ionizing radiation, distinguishing between internal and external radiation, and we applied it to the lifetime occupational history of study subjects, We calculated the Odds Ratio (OR) and its 95% confidence interval (95% CI) for lymphoma (all subtypes combined), B-cell lymphoma, and its major subtypes using unconditional, polytomous logistic regression adjusting for age, gender, and education.ResultsWe did not observe an association between exposure metrics of external and internal radiation and risk of lymphoma (all subtypes), nor with B-cell lymphoma, or its major subtypes, at the levels regularly experienced in occupational settings. An elevated risk of diffuse large B cell lymphoma was observed among the most likely exposed study subjects with relatively higher exposure intensity, which would be worth further investigation.ConclusionsFurther investigation is warranted on risk of B cell lymphoma subtypes associated with low-level occupational exposure to external ionizing radiation, and to clarify whether lymphoma should be included among the cancer outcomes related to ionizing radiation.

The present paper proposes a concept for a water-cooled high temperature unsteady total pressure probe intended for measurements in the hot sections of industrial gas turbines or aero-engines. This concept is based on the use of a conventional miniature piezoresistive pressure sensor, which is located at the probe tip to achieve a bandwidth of at least 40 kHz. Due to extremely harsh conditions and the intention to immerse the probe continuously into the hot gas stream, the probe and sensor must be heavily cooled. The short term objective of this design is to gain the capability of performing measurements at the temperature conditions typically found at high pressure turbine exit (1100-1400 K) and in the long term at combustor exit (2000 K or higher). [DOI: 10.1115/1.2969092]

The ramping trend of cheap and performant single board computers (SBC) is growingly offering unprecedented opportunities in various domains, taking advantage of the widespread support and flexibility offered by an operating system (OS) environment. Unfortunately, data acquisition systems implemented in an OS environment are traditionally considered not to be suitable for reliable industrial applications. Such a position is supported by the lack of hardware interrupt handling and deterministic control of timed operations. In this study, the authors fill this gap by proposing an innovative and versatile SBC-based open-source platform for CPU-independent data acquisition. The synchronized measurement unit (SMU) is a high-accuracy device able to perform multichannel simultaneous sampling up to 200 kS/s with sub-microsecond synchronization precision to a GPS time reference. It exhibits very low offset and gain errors, with a minimum bandwidth beyond 20 kHz, SNR levels above 90 dB and THD as low as −110 dB. These features make the SMU particularly attractive for the power system domain, where synchronized measurements are increasingly required for the geographically distributed monitoring of grid operating conditions and power quality phenomena. We present the characterization of the SMU in terms of measurement and time synchronization accuracy, proving that this device, while low-cost, guarantees performance compliant with the requirements for synchrophasor-based applications in power systems. [ABSTRACT FROM AUTHOR]

The building sector is undergoing a deep transformation to contribute to meeting the climate neutrality goals set by policymakers worldwide. This process entails the transition towards smart energy-aware buildings that have lower consumptions and better efficiency performance. Digitalization is a key part of this process. A huge amount of data is currently generated by sensors, smart meters and a multitude of other devices and data sources, and this trend is expected to exponentially increase in the near future. Exploiting these data for different use cases spanning multiple application scenarios is of utmost importance to capture their full value and build smart and innovative building services. In this context, this paper presents a high-level architecture for big data management in the building domain which aims to foster data sharing, interoperability and the seamless integration of advanced services based on data-driven techniques. This work focuses on the functional description of the architecture, underlining the requirements and specifications to be addressed as well as the design principles to be followed. Moreover, a concrete example of the instantiation of such an architecture, based on open source software technologies, is presented and discussed. [ABSTRACT FROM AUTHOR]

This Special Issue aims at collecting new research contributions and perspectives on the topic of the monitoring and automation of modern power systems. Given the multitude of distributed energy resources to be coordinated, the velocity with which the system conditions may change, and the massive increase of heterogeneous data to be processed, grid operators increasingly need to be supported in their tasks by fully (or semi-) automated grid management processes. In [[3]], a distribution system state estimation algorithm based on an extended Kalman filter (EKF) is proposed for the accurate tracking of the grid operating conditions over time. [Extracted from the article]

This article presents a new Kalman filter (KF) approach to power system state estimation (SE) based on phasor measurement units (PMUs), in which the knowledge of the system frequency is exploited to ensure the accuracy of the estimated quantities even under off-nominal conditions. In the proposed solution, the frequency is added as a new state variable to be estimated so that its value can be known with lower uncertainty, thus leading to more accurate estimates also for node voltages and branch currents. All the frequency measurements available from PMUs can be exploited through the presented method to improve the estimation. In order to assess the benefits given by the integration of the frequency knowledge, the performance of the new approach is compared to different SE methodologies, by means of simulations carried out on the New England IEEE 39-bus system under different realistic operating conditions and measurement configurations. Performed tests take into account, in particular, the possible occurrence of off-nominal frequency conditions, highlighting the issues associated with traditional PMU-based KF approaches and proving the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

This article proposes a forecasting-aided medium-voltage (MV) distribution system state estimation (SE) method using a robust ensemble Kalman filter (REnKF). In the proposed solution, low-voltage (LV) measurements at the LV side of secondary substations are used together with the mathematical model of the MV/LV substations to derive new equivalent MV measurements. This yields the improvement of measurement redundancy and robustness of the REnKF to bad data and unknown system process noise. Specifically, we rely on the temporal correlations of the constructed innovation vector as well as the projection statistics (PS) to detect and modify the measurement error covariance matrix. Furthermore, the system process noise covariance matrix is updated adaptively to mitigate the impacts of uncertainties ON-state forecasting and measurement filtering. Extensive comparisons have been carried out with both the traditional EnKF and other SE formulations under balanced and unbalanced distribution system conditions. The results reveal that our proposed REnKF is able to obtain accurate SE results under various operation conditions, including the presence of intermittent renewable energy sources, bad data, and system unbalance. [ABSTRACT FROM AUTHOR]

While the initial aim of smart meters is to provide energy readings for billing purposes, the availability of these measurements could open new opportunities for the management of future distribution grids. This paper presents a multilevel state estimator that exploits the smart meter measurements for monitoring both low and medium voltage grids. The goal of this paper is to present an architecture that is able to efficiently integrate smart meter measurements and to show the accuracy performance achievable if the use of real-time smart meter measurements for state estimation purposes was enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at different hierarchical levels. The coordination of the estimation levels is realized through a cloud-based infrastructure, which also provides the interface to auxiliary functions and the access to the estimation results for other distribution grid management applications. A mathematical analysis is performed to characterize the estimation algorithm in terms of accuracy and to show the performance achievable at different levels of the distribution grid when using the smart meter data. Simulations are presented, which validate the analytical results and demonstrate the operation of the multilevel estimator in coordination with the cloud-based platform. [ABSTRACT FROM AUTHOR]

The effective management of future smart grids is strictly related to the accurate knowledge of the network operating conditions via distribution system state estimation (DSSE). To achieve this target, the measurement infrastructure of the distribution systems needs a substantial upgrade. However, obvious economic limitations prevent a massive deployment of measurement instruments on the field in short times. As a consequence, ad hoc meter placement techniques have to be applied to define location and type of a minimum set of measurements required to obtain the desired accuracy performance. This paper presents a mathematical analysis showing the impact of current and power measurements on the accuracy of DSSE results. The goal is to provide the analytical tools to identify the best placement for additional current or power measurements when applying an incremental deployment of meters in the distribution system. The analysis has a general validity but has also a clear impact on practical situations when the monitoring systems are upgraded starting from an existing infrastructure. Tests on a 95-bus sample grid are presented in order to validate the found mathematical results and to highlight the associated benefits in a meter placement perspective. [ABSTRACT FROM AUTHOR]

This article presents a new Kalman filter (KF) approach to power system state estimation (SE) based on phasor measurement units (PMUs), in which the knowledge of the system frequency is exploited to ensure the accuracy of the estimated quantities even under off-nominal conditions. In the proposed solution, the frequency is added as a new state variable to be estimated so that its value can be known with lower uncertainty, thus leading to more accurate estimates also for node voltages and branch currents. All the frequency measurements available from PMUs can be exploited through the presented method to improve the estimation. In order to assess the benefits given by the integration of the frequency knowledge, the performance of the new approach is compared to different SE methodologies, by means of simulations carried out on the New England IEEE 39-bus system under different realistic operating conditions and measurement configurations. Performed tests take into account, in particular, the possible occurrence of off-nominal frequency conditions, highlighting the issues associated with traditional PMU-based KF approaches and proving the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

The real-time monitoring and control of distribution systems are an essential step toward the future smart grid. This paper presents an interval state estimation model for low-voltage (LV) systems based on smart meter data provided by electrical customers. The formulation is based on a two-level approach, constituted of a nodal processor and a system state estimator. The nodal processor filters the smart meter data and provides a smaller amount of clean processed data to the upper level system state estimator. The system state estimator, based on the extreme value theory, provides the maximum and minimum values of the system states. To obtain narrow interval images, a scaling parameter is used to consider the fact that the errors of downstream smart meters are rarely at the maximum values simultaneously. The presented approach allows removing the assumption on the particular probability distribution of the smart meter measurement errors. Simulations are carried out on a sample LV network to show the characteristics and performance of the proposed approach. [ABSTRACT FROM AUTHOR]

To deal with the increasing complexity of distribution networks that are experiencing important changes, due to the widespread installation of distributed generation and the expected penetration of new energy resources, modern control applications must rely on an accurate picture of the grid status, given by the distribution system state estimation (DSSE). The DSSE is required to integrate all the available information on loads and generators power exchanges (pseudomeasurements) with the real-time measurements available from the field. In most cases, the statistical behavior of the measured and pseudomeasured quantities cannot be approximated by a Gaussian distribution. For this reason, it is necessary to design estimators that are able to use measurements and forecast data on power flows that can show a non-Gaussian behavior. In this paper, a DSSE algorithm based on Bayes’s rule, conceived to perfectly match the uncertainty description of the available input information, is presented. The method is able to correctly handle the measurement uncertainty of conventional and synchronized measurements and to include possible correlation existing between the pseudomeasurements. Its applicability to medium voltage distribution networks and its advantages, in terms of accuracy of both estimated quantities and uncertainty intervals, are demonstrated. [ABSTRACT FROM PUBLISHER]

This paper presents the wideband system identification (WSI) technique, i.e., an online method to identify power impedances over a wide frequency range from which the corresponding parametric impedance can be calculated online as well. The WSI technique exploits an existing custom 25-kW power electronic converter on the top of its power conversion function, which serves as power amplifier of an existing power hardware-in-the-loop (PHiL) simulation setup. The PHiL simulation technique allows connecting a real device under test (DUT) with the real-time simulated rest of system (ROS) at power level. An interface algorithm (IA) on simulation side and a power amplifier (the 25-kW power electronic converter) connect ROS and DUT. This paper shows the impact of the uncertainties in the WSI chain on the accuracy of the impedance identification and highlights how the WSI technique can be combined with the damping impedance method IA to improve both accuracy and stability of the PHiL test bench. The application of the method is illustrated for the scenario of a PHiL test of a dc microgrid with a passive load. [ABSTRACT FROM PUBLISHER]

Distribution system state estimation (DSSE) is an essential tool for the management and control of future distribution networks. Distribution grids are usually characterized by a very large number of nodes and different voltage levels. Moreover, different portions of the system can be operated by different distribution system operators. In this context, multiarea approaches are key tools to efficiently perform DSSE. This paper presents a novel approach for multiarea state estimation in distribution systems. The proposed algorithm is based on a two-step procedure, where the first-step local estimations are refined through a newly designed second step that allows the integration of the measurement information available in the adjacent areas. The main novelty in this paper is the mathematical analysis of the impact brought by possible measurements shared among different areas, which drives the design of a new efficient weighted least squares formulation of the second step to maximize the achievable estimation accuracy. Tests performed on the unbalanced IEEE 123-bus network prove the goodness of the new multiarea estimator proposed and show the accuracy and efficiency enhancements obtainable with respect to previous literature. [ABSTRACT FROM PUBLISHER]

Distribution system state estimation (DSSE) tools are required to estimate the real operating conditions of power distribution networks. The accuracy of the quantities estimated through DSSE plays a key role in the effectiveness of the management and control functions. This paper presents a comprehensive mathematical analysis aimed at highlighting the most important factors affecting the accuracy of the voltage profile obtained by means of weighted least squares estimators. The proposed analysis shows the way in which the uncertainty components of the voltage estimations depend on the measurement system available on the field. In particular, this paper considers synchrophasor measurements provided by phasor measurement units and hybrid measurement systems also presenting conventional devices. Tests performed on a 95-bus network prove the validity of the theoretical analysis and show the importance of the results also from a meter placement perspective, emphasizing the requirements needed to achieve specific accuracy targets. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the distribution system state estimation in wide-area networks. The main goal of this paper is to present a two-step procedure designed to accurately estimate the status of a large-scale distribution network, relying on a distributed measurement system in a multiarea framework. First of all, the network is divided into subareas, according to geographical and/or topological constraints and depending on the available measurement system. Then, in the first step of the estimation process, for each area, a dedicated estimator is used, exploiting all the measurement devices available on the field. In the second step, data provided by local estimators are further processed to refine the knowledge on the operating conditions of the network. To improve the accuracy of the estimation results, correlation arising in the first step estimations has to be suitably evaluated and considered during the second step. Performed analysis shows that existing correlations can be included in the estimation process with very low data exchange among areas, thus involving minimum communication costs. Both first and second steps can be performed in a decentralized way and with parallel processing, thus leading to reduced overall execution times. Test results, obtained on the 123-bus IEEE test network and proving the goodness of the proposed method, are presented and discussed. [ABSTRACT FROM PUBLISHER]

Smart Grid (SG) is an expression that has diffused worldwide. However, the meaning of this expression is not commonly shared, since there is not yet a totally defined or accepted idea of smartness. What is commonly recognized is that significant changes are occurring in electric systems, and traditional management and protection schemes will not be suitable for next generation electric grids [1]. [ABSTRACT FROM PUBLISHER]

Distribution system state estimation (DSSE) is one of the key elements of the monitoring activity of an active distribution network, and is the basis for every control and management application. The DSSE relies on real measurements collected by the distributed measurement system and on other available information, mainly obtained from historical data that help in obtaining observability. This prior information is necessary to derive the so called pseudomeasurements. Accurate input data are fundamental for an accurate estimation, as well as knowledge on possible correlation in the measured and pseudomeasured data. A degree of correlation can exist in the measured data, due to measurement devices, and among power consumptions or generations of some particular nodes. This paper presents an exhaustive analysis on the influence of correlations on the quality of the estimation. The importance of including correlation in the weighted least square estimation approach is discussed using both traditional and synchronized measurements. Results obtained on a 95-bus distribution network are presented and discussed. [ABSTRACT FROM AUTHOR]

The distribution grid is the infrastructure that transports electrical energy generated by large plants that are long distances away to the final user. A typical distribution grid consists of High-to-Medium Voltage (HV/MV) transformation centers; the MV grid; Medium-to-Low Voltage (MV/LV) transformation centers; and the LV grid, both three-phase and single-phase. Residential and commercial customers are mainly connected to the LV, while large industrial facilities are connected to the MV. [ABSTRACT FROM PUBLISHER]

Deregulation and privatization actions are creating new problems of control, management and reliability, because of new players and new technologies spreading in distribution systems. Such new scenarios require more comprehensive and accurate knowledge of the system to make control actions efficient and reliable. In particular, attention must be paid to phase angles estimation to avoid critical situations. In this context, the use of phasor measurement units (PMUs) looks promising. This paper presents an efficient branch-current-based distribution systems state estimation. The estimator allows synchronized phasor measurements provided by PMUs to be included. In addition, the branch current state model is extended so that the knowledge of the voltage profile is significantly improved. The estimator is expressed both in polar and rectangular coordinates and a comparison between the obtainable accuracy and computational efficiency is presented. Furthermore, the possibility to treat radial and weakly meshed topology, also in presence of dispersed generation, is analyzed. The results obtained on different distribution networks are presented and discussed. [ABSTRACT FROM AUTHOR]

The main issue in this paper is the real-time simulator of a part of a power grid with a wind turbine. The simulator is constructed on the basis of a classic PC running under a classic operating system. The proposed solution is expected and desired by users who intend to manage power microgrids as separate (but not autonomous) areas of common national power systems. The main reason for the decreased interest in real-time simulators solutions built on the basis of PC is the simulation instability. The instability of the simulation is due to not keeping with accurate results when using small integration steps and loss of accuracy or loss of stability when using large integration steps. The second obstacle was due to the lack of a method for integrating differential equations, which gives accurate results with a large integration step. This is the scientific problem that is solved in this paper. A new solution is the use of a new method for integrating differential equations based on average voltage in the integration step (AVIS). This paper shows that the applied AVIS method, compared to other methods proposed in the literature (in the context of real-time simulators), allows to maintain simulation stability and accurate results with the use of large integration steps. A new (in the context of the application of the AVIS method) mathematical model of a power transformer is described in detail, taking into account the nonlinearity of the magnetization characteristics. This model, together with the new doubly-fed induction machine model (described in the authors' previous article), was implemented in PC-based hardware. In this paper, we present the results of research on the operation states of such a developed real-time simulator over a long period (one week). In this way, the effectiveness of the operation of the real-time simulator proposed in the paper was proved. [ABSTRACT FROM AUTHOR]

Direct Current (DC) grids are considered an attractive option for integrating high shares of renewable energy sources in the electrical distribution grid. Hence, in the future, Alternating Current (AC) and DC systems could be interconnected to form hybrid AC-DC distribution grids. This paper presents a two-step state estimation formulation for the monitoring of hybrid AC-DC grids. In the first step, state estimation is executed independently for the AC and DC areas of the distribution system. The second step refines the estimation results by exchanging boundary quantities at the AC-DC converters. To this purpose, the modulation index and phase angle control of the AC-DC converters are integrated into the second step of the proposed state estimation formulation. This allows providing additional inputs to the state estimation algorithm, which eventually leads to improve the accuracy of the state estimation results. Simulations on a sample AC-DC distribution grid are performed to highlight the benefits resulting from the integration of these converter control parameters for the estimation of both the AC and DC grid quantities. [ABSTRACT FROM AUTHOR]

The development of strategies for distribution network management is an essential element for increasing network performance and reducing the upgrade of physical assets. This paper analyzes a multi-timescale framework to control the voltage of distribution grids characterized by a high penetration of renewables. The multi-timescale solution is based on three levels that coordinate Distributed Generation (DG) and Energy Storage Systems (ESSs), but differs in terms of the timescales and objectives of the control levels. Realistic load and photovoltaic generation profiles were created for cloudy and clean sky conditions to evaluate the performance features of the multi-timescale framework. The proposed solution was also compared with different frameworks featuring two of the three levels, to highlight the contribution of the combination of the three levels in achieving the best performance. [ABSTRACT FROM AUTHOR]

The paper presents a method for the frequency characterization of voltage transformers (VTs) for medium voltage (MV) grids which involves only sine waves. It is called extended SINDICOMP, since it is an extended version of the previously developed technique SINDICOMP. It requires, in the first step, an evaluation and a compensation of the non-linearity introduced by the VT when it is supplied with a 50 Hz sinusoidal input at rated value. Then, the VT is characterized with a low voltage sinusoidal frequency sweep from the second harmonic frequency up to the first resonance frequency. Some rules to build the approximated frequency response, starting from these two sets of data, are given in the paper. The proposed approach is applied to three commercial MV VTs. Significant improvement of the VT performance is obtained, compared to the use of a frequency response obtained from the low voltage characterization. [ABSTRACT FROM AUTHOR]

The design of intelligent strategies for grid management is a cost-effective solution to increase the hosting capacity of distribution grids without investing in the reinforcement of the grid assets. This paper presents a distributed voltage control algorithm to coordinate Energy Storage Systems (ESSs) and Distributed Generation (DG) in a scenario of high renewable penetration. The proposed control algorithm relies on a dual decomposition approach and aims at mitigating possible voltage rise events occurring in the Low Voltage (LV) grid by solving an optimization problem of power minimization. Instead of using local control strategies, in the proposed solution, the voltage control burden is distributed among all the available resources in the grid, which cooperate to resolve the existing voltage violations. The performance of the developed voltage control has been tested under realistic distribution grid scenarios, using stochastic load profiles together with photovoltaic generation profiles obtained in the presence of both clear sky and cloudy sky conditions. The algorithm is also compared to a strategy that considers only DG management, highlighting the benefits associated to the proposed coordination of DG and Energy Storage Systems (ESSs). [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the genetic variability and population structure of Taenia multiceps , and to correlate morphological features of individual coenuri with haplotypes. A total of 92 animals (86 sheep; 4 goats; 1 cattle; 1 mouflon, Ovis musimon ) aged between 6–36 months showing clinical symptoms of cerebral coenurosis were included in this study. T. multiceps coenuri (n = 118) sampled from live animals during routine surgery procedures or at post-mortem inspections were examined morphologically and molecularly identified. Morphological features of the 52 coenuri selected for this study (number and size of large and small hooks) were within the range reported in the literature. Fifty-two of the molecularly confirmed T. multiceps coenuri harboured by 47 animals (sheep = 41; cattle = 1; goats = 4; mouflon = 1) were used to determine gene genealogies and population genetic indices and were compared to the 3 T. multiceps genetic variants, Tm1–Tm3 previously described from Sardinia, Italy. For the 379 bp cox1 dataset we identified 11 polymorphic sites of which 8 were parsimony informative. A high haplotype diversity (0.664 ± 0.067) was recorded for the cox1 sequences defining 10 haplotypes (TM01–TM10). The comparison of haplotypes generated in this study with published T . multiceps Tm1 variant pointed to the possible existence of a common lineage for T. multiceps . No correlation was detected between the size of the small and large hooks and the cox 1 haplotypes. Polycystic infestation (2–9 coenuri) was recorded in 27.7% of animals (13/47). No statistical correlation between polycystic T. multiceps infection and haplotypes was detected. [ABSTRACT FROM AUTHOR]

Background and Aim: Job burnout has been recognized as a serious occupational hazard among professionals, such as health care professionals. The sense of coherence (SoC) is deemed to be a personal resource capable of reducing the impact of job stressors and, consequently, the experience of job burnout. The purpose of this study was to investigate the relationship between SoC and job burnout among speech and language therapists., Materials and Methods: A descriptive and cross-sectional analysis was carried out through an online self-reported questionnaire. A total of 217 Italian speech and language therapists were involved in the study. The Anova test, T-test and logistic regression were performed to study the association between SoC and job burnout., Results: The Anova test showed that job tenure was not associated to job burnout. The T-test showed that speech and language therapists having a low SoC exhibited significantly higher emotional exhaustion, higher cynicism, and lower professional efficacy (t=-7.190 d.f.=215 p<.001) when compared to those having a high SoC. Finally, the odds ratio showed that low SoC was associated with high emotional exhaustion (OR=11.86; 95% CI=5.52-25.49; p<0.05), low SoC was associated with high cynicism (OR=4.41, CI=2.50-7.80; p<0.05), and low SoC was associated with low personal efficacy (OR=4.70; CI=2.59-8.52; p<0.05)., Conclusion: Our results are in line with previous studies which showed that SoC is a fundamental personal resource which may activate workers' reaction to various stressors, thus reducing the experience of burnout.