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In this contribution, the authors focus on the use of a metasurface (physically implemented as a 2D array of spiral resonators) as an additional component of a two-coil Wireless Power Transfer (WPT) system, with the aim of increasing the robustness to misalignment between the transmitter and the receiver coils. Resonator arrays have been proven to have a positive effect on WPT systems’ performance since they produce a focusing effect on the magnetic field; at the same time, they contribute to the reduction of the electric near field. In addition, we herein demonstrate how proper control over the metasurface’s unit cells can contribute to making a WPT system more tolerant to misalignment. In particular, the comparison between metasurfaces of different sizes (keeping the same transmitting and receiving coils) and their optimization performed to improve misalignment robustness is proved by numerical simulations.

The aim of the manuscript is to present a feasibility analysis for misalignment recovering purposes in a two coils Wireless Power Transfer system with the presence of a Spiral Resonators Array. The misalignment is faced by opportunely varying the values of capacitive loads with an ad-hoc optimization procedure. In a previous work, the authors have already presented a methodology able to compensate the system efficiency drop by applying a postprocessing approach based on an S-parameters matrix circuit representation of the entire system, and by changing the values of the capacitors which opportunely load each element of the SRA structure. Here, an optimization procedure followed by a Montecarlo method have been used in order to investigate the effect of the components tolerances and the sensitivity of the system. In this way, a more realistic modelization can be accomplished, guiding the designer to the realization of a more robust system.

In the present work the flexibility of an array of resonators applied to a simple 2 coils Wireless Power Transfer system has been presented with the aim of simultaneous Power and Data Transfer.

In this paper, the authors introduce a semianalytical model for the analysis and the design of a permanent magnet (PM) tubular linear generator intended for electrical energy generation from sea waves. The translator of the analyzed machine is constituted by axially magnetized ferrite PMs with alternating polarity and soft magnetic pole pieces in between; a two-pole, double-layer, three-phase winding is located in the slots of the stator. The presented model, based on the use of the Carter coefficient and of the Fourier transform in the direction of the motion, is able to take into account the end effects due to the finite length of the stator. The presence of slots and teeth is subsequently considered by some postprocessing calculation carried on the results of the semianalytical model. Comparison with a finite element analysis and measurements taken on a prototype has been performed to validate the presented model. The model can be easily extended to other translator typologies, e.g., to air core translator with Halbach array of NdFeB PMs.

The scientific literature lacks methodologies for assessing HRES investments because of complex interactions among HRES components, conflicting objectives, and uncertainty in the energy demand. This work fills this gap by presenting a methodology to assess long-term investments in HRES for off-grid buildings. To strengthen the reliability of the assessment, the methodology exploits an HRES optimal synthesis, sizing and operation simulating the system and embodying the interactions among HRES components for both electrical and heat generation, the building loads evaluation, and the verification of the effect of uncertainties in the energy demand. These aspects have already been investigated, but, to our best knowledge, have never been considered in a concurrent and integrated way. The resulting designs are optimised through an NSGA-II algorithm that minimises the system CO2 emissions and differential cost with respect to a solution that does not use renewable energy sources. The soundness of the solutions is evaluated through a scenario analysis and a sensitivity analysis using the Morris method. The applicability of the proposal was verified through a case study in an off-grid facility, showing possible savings up to 47 k€ and 320 tCO2. The methodology is thus strongly consistent with the building decarbonisation addresses set by the policymakers.

This paper illustrates and compares the ability of several clustering algorithms to correctly associate a given aggregate daily electrical load curve with its corresponding day of the week. In particular, popular clustering algorithms like the Fuzzy c-Means, Spectral Clustering and Expectation Maximization are compared, and it is shown that the best results are obtained if the daily data are compressed with respect to a single feature, namely the so-called “Morning Slope”. Such a feature-based clustering appears to outperform the clustering results obtained upon using other classic features, and also with respect to using other conventional compression methods, such as the Principal Component Analysis, in all the examined European countries. This result is particularly interesting, as this feature provides a direct physical interpretation that can be used to obtain insights on the structure of the daily load profiles.

Deep Learning (DL)-based methods are becoming of interest in the power community thanks to their ability to extract information and patterns in large sets of input data. In this paper, we exploit Convolutional Neural Networks (CNNs) to process the information of wind speed forecasts at different altitudes to improve the accuracy of the prediction of wind power generation. Through extensive comparisons on actual hourly data from four wind farms in Italy, we show that the forecasts of the wind components at different altitudes do provide an advantage in obtaining accurate forecasts. In doing so, we also evaluate the importance of other design aspects, such as the choice of filters and activation functions, in the implementation of the methodology. Finally, results are compared with other more conventional benchmark prediction algorithms as well.

Optimal design and operation of energy systems serving clusters of buildings interconnected by energy microgrids is a scientific challenge for the engineering community, with many interdisciplinary aspects involved. In the paper, the optimization problem is tackled in terms of simulation-based design of the energy system for a typical year of operation. The methodology has been applied to a Campus in Trieste, Italy, involving locally available renewable energy sources, a centralized cogeneration system, and decentralized heat pumps. The nominal powers of cogeneration plant, photovoltaic modules and wind turbine have been optimized by a population-based evolutionary optimization algorithm, previously proposed by the authors. We have also found the optimal scheduling of energy generators by means of a greedy approach. The solution with maximum Annualized Cost Saving Percentage is discussed, highlighting how a configuration involving decentralized heat pumps is cost effective, integrates more renewable energy sources, and reduces environmental impact and grid exchange compared to a benchmark solution, with fully centralized generators.

A data-based estimation of the wind–power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data-based procedure to build a robust and accurate estimate of the wind–power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance-based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut-in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance.

Guided wave (GW) testing is regularly used for finding defect locations through long-range screening using low-frequency waves (from 5 to 250 kHz). By using magnetostrictive sensors, some issues, which usually limit the application to nuclear power plants (NPPs), can be fixed. The authors have already shown the basic theoretical background and simulation results concerning a real steel pipe, used for steam discharge, with a complex structure. On the basis of such theoretical framework, a new campaign has been designed and developed on the same pipe, and the obtained experimental results are now here presented as a useful benchmark for the application of GWs as nondestructive techniques. Experimental measures using a symmetrical probe and a local probe in different configurations (pulse-echo and pitch-catch) indicate that GW testing with magnetostrictive sensors can be reliably applied to long-term monitoring of NPPs components.

In this paper we present a novel method for daily short-term load forecasting, belonging to the class of “similar shape” algorithms. In the proposed method, a number of parameters are optimally tuned via a multi-objective strategy that minimizes the error and the variance of the error, with the objective of providing a final forecast that is at the same time accurate and reliable. We extensively compare our algorithm with other state-of-the-art methods. In particular, we apply our approach upon publicly available data and show that the same algorithm accurately forecasts the load of countries characterized by different size, different weather conditions, and generally different electrical load profiles, in an unsupervised manner.

The paper proposes a decentralized stochastic con- trol of microgrids aimed at improving both system frequency stability and microgrids’ revenue. The proposed control decides, based on a set of stochastic rules, a measure of the frequency and microgrid operating conditions, whether each microgrid should adopt a cooperative strategy where primary frequency regulation services are provided to the grid or a non-cooperative strategy, where microgrids aim at maximizing their revenues. Simulations, based on the Monte Carlo approach, are performed on the IEEE 39 Bus System in order to validate the robustness of the proposed control strategy.

Currently, narrowband Power line communication (PLC) is considered an attractive communication system in smart grid environments for applications such as advanced metering infrastructure (AMI). In this paper, we will present a comprehensive comparison and analysis in time and frequency domain of noise measured in China and Italy. In addition, impulsive noise in these two countries are mainly analyzed and modeled using two probability based models, Middleton Class A (MCA) model and α stable distribution model. The results prove that noise measured in China is rich in impulsive noise, and can be modeled well by α stable distribution model, while noise measured in Italy has less impulsive noise, and can be better modeled by the MCA model.

Currently, narrowband Power line communication (PLC) is considered as an attractive communication system in smart grid environments for applications such as advanced metering infrastructure (AMI). In this paper, we will present a comprehensive comparison and analysis in time and frequency domain of noise measured in China and Italy. In addition, impulsive noise in these two countries are mainly analyzed and modeled using two probability based models, Middleton Class A (MCA) model and \(\alpha\) stable distribution model. The results prove that noise measured in China is rich in impulsive noise, and can be modeled well by \(\alpha\) stable distribution model, while noise measured in Italy has less impulsive noise, and can be better modeled by MCA model.

Nondestructive testing (NDT) techniques are widely used as a reliable way for preventing failures and helping in the maintenance design and operation of critical infrastructures and complex industrial plants as nuclear power plants (NPPs). Among the NDT techniques, guided waves (GWs) are a very promising technology for such applications. GWs are structure-borne ultrasonic waves propagating along the structure confined and guided by its geometric boundaries. Testing using GWs is able to find defect locations through long-range screening using low-frequency waves (from 5 to 250 kHz). The technology is regularly used for pipe testing in the oil and gas industry. In the nuclear industry, regulators are working to standardize monitoring and inspection procedures. To use the technology inside an active plant, operators must solve issues like high temperatures (up to more than 300 C inside a light-water reactor’s primary piping), high wall thickness of components in the primary circuit, and characteristic defect typologies. Magnetostrictive sensors are expected to overcome such issues due to their physical properties, namely, robust constitution and simplicity. Recent experimental results have demonstrated that magnetostrictive transducers can withstand temperatures close to 300 C. In this paper, the GW technology will be introduced in the context of NPPs. Some experimental tests conducted using such a methodology for steel pipe having a complex structure will be described, and open issues related to high-temperature guided wave applications (e.g., wave velocity or amplitude fluctuations during propagation in variable temperature components) will be discussed. Copyright VC 2017 by ASME

This paper compares the impact of microgrids on the transient response of the transmission system, and, more specifically, on its frequency variations, when different control strategies and different storage capacities are employed. Extensive Monte Carlo simulations are performed based on the IEEE 39-bus system, and show that the dynamic behavior of the transmission system is affected in a non-trivial way by the control strategy of the microgrids energy management system and their storage capacity.

NarrowBand PLC (NB-PLC) are presently considered as an attractive communication system in the so called advanced metering infrastructure (AMI). Noise characterization is then very useful to determine the actual potential of NB-PLC for communication services towards the so called home prosumer in the frequency range 10–500kHz. In this paper we will present a comprehensive comparison and analysis of noise injected by many in-home devices in the whole NB frequency range. A not so common characteristic of the paper is the comparison among data collected in different countries, i.e. China and Italy.

In this paper, the numerical analysis on an air-core pulsed alternator is presented. Since compulsators are characterized by very fast electromechanical transients, their accurate analysis requires strong coupling between the equations governing the electrical and the mechanical behaviors. The device is investigated by using a dedicated numerical code capable to take into account eddy currents, compensating windings, as well as the excitation/control circuits. Furthermore, the code is capable of modeling centrifugal forces and vibrations acting on the shaft due to electric and mechanical unbalances or to misalignments of the shaft from its centered position. This makes the code a very powerful tool during the design phase. Examples of application of the code to machines with nonuniform compensating shield and with shorted compensating windings are shown.

Guided Wave (GW) testing is regularly used for finding defect locations through long range screening using low-frequency waves (from 5 to 250 kHz) [1]-[3]. Magnetostrictive sensors can overcome some issues, which usually limit the application to Nuclear Power Plants (NPPs) [4], like for example, high temperatures [5]-[6], high wall thickness of components in the primary circuit, and characteristic defect typologies. The authors have already shown the basic theoretical background, some simulations and some first experimental results concerning a real steel pipe, used for steam discharge, having a complex structure. Collecting more experimental data with a novel test campaign on the same pipe its complex structure results as a useful benchmark for the application of GWs as Non Destructive Techniques (NDT). Experimental measures using a symmetrical probe and a local probe in different configurations (pulse-echo and pitch-catch) indicate that GW testing with magnetostrictive sensors can be reliably applied to long-term monitoring of NPP components.

Predictive Maintenance, Prognostics and Reliability Centered Maintenance approach are becoming more and more important in the railway sector to reduce costs of operation and to increase reliability and safety. In fact, these are fundamental to optimize the maintenance process, to define new measures and algorithms which locate faults, to monitor health conditions of subsystems and to estimate residual life of components. In some cases it's possible to use existing sensors and existing processing hardware to extract new information from the existing available data. It's clear that this is usually the best option because the benefit can be achieved with little or no cost at all. This paper describes the result of a study performed with the aim of detecting arcing events without the need of additional equipment mounted on board the train. A set of data relative to voltage and current collected on high speed trains along with a set of measurements coming from photosensors are available. The data are processed by the use of an advanced classification technique, Support Vector Machines, with the aim of extracting important information such as the time coordinate related to anomalies in the overhead contact line and the status of the pantograph contact strip.

The paper proposes a stochastic model to analyse the dynamic coupling of the transmission system, the electricity market, and microgrids. The focus is on the impact of microgrids on the transient response of the system and, in particular, on frequency variations. Extensive Monte Carlo simulations are performed on the IEEE 39-bus system, and show that the dynamic response of the transmission system is affected in a nontrivial way by both the number and the size of the microgrids. European Commission Horizon 2020 Science Foundation Ireland