Marco Prioli

Fuzzy Metrology-Sound Approach to the Identification of Sources Injecting Periodic Disturbances in Electric Networks

The identification of sources injecting periodic disturbances in electric systems is a critical point in the assessment of the electric power quality. The available methods, based on a deterministic approach, do not always provide correct results. Methods based on heuristic approaches, such as those based on a fuzzy inference system (FIS), provide better results but do not allow measurement uncertainty to be evaluated in a straightforward way. This paper applies a modified FIS to the identification of the sources producing periodic distortion in power systems. The method associates an index, provided together with its measurement uncertainty, to each load connected to a point of common coupling capable of assessing whether the load is injecting or suffering distortion and quantifying the severity of the injected or suffered distortion.

The 16 T Dipole Development Program for FCC

A key challenge for a future circular collider (FCC) with centre-of-mass energy of 100 TeV and a circumference in the range of 100 km is the development of high-field superconducting accelerator magnets, capable of providing a 16 T dipolar field of accelerator quality in a 50 mm aperture. This paper summarizes the strategy and actions being undertaken in the framework of the FCC 16 T Magnet Technology Program and the Work Package 5 of the EuroCirCol.

The Construction of Joint Possibility Distributions of Random Contributions to Uncertainty

The evaluation and expression of uncertainty in measurement is one of the fundamental issues in measurement science and challenges measurement experts especially when the combined uncertainty has to be evaluated. Recently, a new approach, within the framework of possibility theory, has been proposed to generalize the currently followed probabilistic approach. When possibility distributions are employed to represent random contribution to measurement uncertainty, their combination is still an open problem. This combination is directly related to the construction of the joint possibility distribution, generally performed by means of t-norms. The problem is that joint possibility distributions strongly depend on the considered t-norm, and, therefore, the choice of one particular t-norm over another has to be justified. The first goal of this paper is, hence, to provide support to the choice of a particular t-norm. The second goal is to discuss the construction of the joint possibility distribution when the two random contributions to uncertainty show mutual dependence, with specific reference to a particular class of possibility distributions.

Processing Dependent Systematic Contributions to Measurement Uncertainty

In the measurement field, the correlation of two uncertainty contributions is a form of probabilistic association that can significantly affect the final uncertainty associated to the measurement result. The Guide to the expression of uncertainty in measurement recommends a mathematical approach to deal with correlated random contributions to measurement uncertainty. A similar kind of association, or dependence, can characterize also different systematic contributions to uncertainty and should be taken into account when evaluating their effect on the final measurement uncertainty. This paper discusses a new approach to handle such systematic contributions when they are represented by symmetric possibility distributions (PDs) of the same shape. This method allows one to build the joint PD of two systematic contributions, both dependent and independent, and propagate them through a generic measurement function.

A Metrological Comparison Between Different Methods for Harmonic Pollution Metering

Several proposals can be found, in the literature, to split the responsibility for the injection of harmonic pollution between the different loads and between loads and source. The methods have been tested mainly by means of simulations performed on an industrial test system identified by the IEEE Task Force on Harmonics Modeling and Simulation. The main problem of the simulation results is that they tend to disregard measurement uncertainty that, if not properly considered, might lead to an incorrect assessment of responsibility. This paper is therefore aimed at comparing the different available methods from a metrological point of view, in order to provide a wider and more complete basis for evaluating their performances.

A 2-D Metrology-Sound Probability–Possibility Transformation

In recent years, the possibility theory has been investigated by many authors in the field of mathematics and engineering. A possibility distribution (PD) is, from the mathematical point of view, a generalization of a probability distribution, since it can represent the upper envelope of a family of probability distributions. Given a probability distribution, different probability-possibility transformations can be applied to transform the probability distribution into different PDs. Probability–possibility transformations are useful in metrology whenever statistical data must be dealt with inside the possibility theory, for instance, when they have to be associated with other kinds of uncertain and imprecise data to evaluate measurement uncertainty. This paper shows how uncorrelated and correlated random contributions to uncertainty can be effectively represented, processed, and combined in terms of PDs, by means of an original probability–possibility transformation.

A Consistent Simulation of Electrothermal Transients in Accelerator Circuits

Transient effects occurring in a superconducting accelerator circuit can be correctly simulated only if the models consistently account for the electrothermodynamic coupling between the magnets, the protection systems, and the remaining network. We present a framework based on the idea of cosimulation. The core component is a coupling interface exchanging information between the independent models. Within the framework, we simulate selected parts of a magnet and the electrical network, combining appropriately different commercial tools. This modularity gives the possibility of integrating new tools in the framework, to provide further insights on different physical domains as mechanics or fluid dynamics. The workflow is applied to the field-circuit coupling of an LHC main dipole magnet.

Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions

A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach.

Uncertainty propagation through non-linear measurement functions by means of joint Random-Fuzzy Variables

A still open issue, in uncertainty evaluation, is that of asymmetrical distributions of the values that can be attributed to the measurand. This problem becomes generally not negligible when the measurement function is highly non-linear. In this case the law of uncertainty propagation suggested by the GUM is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of Random-Fuzzy Variables. Under this approach, also non-random contributions to uncertainty can be considered. An example of application is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach.

2D Probability-Possibility Transformations

Probability-possibility transformations are useful whenever probabilistic information must be dealt with in the possibility theory. In this paper, two-dimensional probability-possibility transformations of joint probability densities are considered, to build joint possibilities such that the marginals preserve the same information content as the marginals of the joint probability densities.