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The transmission of an electromagnetic field produced by a current loop of finite radius through a coaxial circular aperture in a perfectly conducting plate is evaluated through a rapidly convergent formulation in an exact form. By applying the equivalence principle, the problem is first formulated in the Hankel transform domain, obtaining a set of dual integral equations in which the equivalent surface magnetic current density defined on the aperture is not known. The set of dual integral equations is regularised in a second‐kind Fredholm integral equation by applying the Abel integral‐transform technique. The solution is achieved by expanding the unknown in a set of orthogonal basis functions that correctly reproduce the behaviour of the equivalent magnetic current at the edge of the aperture. Finally, under particular assumptions, a low‐frequency solution is extracted in a closed form. Numerical results are reported to validate the accuracy and efficiency of the proposed formulations.

The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this article is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks.

This work investigates voltages on overhead distribution lines produced by direct strokes, protected by shield wires, placed both above and below phase conductors. An improved formulation based on transfer matrix method and theory of multiconductor transmission lines with periodic grounding allows to eliminate the need of two characteristic wave impedance matrices. Based on the theoretical formulation developed in the first part of this work, as well as on the models there presented for the pole and grounding systems, we report here a wealth of simulation results concerning the line overvoltages subsequent to a pole being hit by lightning. Results are discussed considering a variety of factors, namely, position of the victim pole, type of load at both ends of the line, presence or absence of underbuilt shield wires, waveform of the lightning current, soil parameters and parameters of the frequency-dependent impedance of the pole grounding system. The good performance of the developed method is assessed by comparing our results with some EMTPRV simulations. The transfer matrix method is then used to show the effect of shield wires in conjunction with periodic grounding: we assess the effects of terminations, frequency dependent models of the pole footing and grounding systems and frequency dependent models of lossy ground.

Hans Christian ?ersted (Figure 1) was a professor of physics at the University of Copenhagen (1777-1851). When he learned of the invention of the electric battery in 1799 from Alessandro Volta, he began to experiment with electric currents.

The modern revolutionary changes in power delivery systems with the advent of smart and flexible grids require systems interoperability, seamless integration of technologies and functionalities. This scenario is paving the way to new prospective of energy clusters that call for new methodologies for the dynamic energy management of distributed energy resources. This paper proposes a new management scheme of energy clusters (e.g., smart building, energy community or virtual power plant) that is based on a data driven decision-maker that daily deploys the activities of the day ahead, providing an optimized scheduling which will be the base for the operations for the next day. The new aggregator relies on some innovative features. It leverages an optimization process based on the elastic net regularization that proves to be an effective support for finding the best scheduling of the distributed resources according to specific key performance indicators, that presently is the unbalance. The decision process works on predicated data obtained through a recently assessed short term forecasting based on long short-term memory neural networks properly adapted to distributed environments. The method allows to extract insight from data and run what-if scenarios to define the best scheduling of resources. The technique uses a set of feasible rules to optimally aggregate the distributed resources and demand-side management programs. We tested the proposed aggregator on a real energy cluster making use of real measured data over two years period. The results show the effectiveness of the proposed approach that is able to predict any sort of unit and manage any sort of program. The significance of this work is to approach the energy management as an optimization and decision problem in a more robust way of reasoning, by employing forecasting/optimization over all the quantities in the community, resulting in a thoroughly complex and efficient method.

In this paper, that is an extended version of a work [1] presented at the 2019 International Symposium on Lightning Protection (XV SIPDA), a survey of analytical solutions available in the literature for the evaluation of the lightning-induced voltages in case of both step and linearly rising channel-base currents is presented. Two tools, CiLIV and LIV, based on some of the above mentioned analytical solutions are also presented. Finally, a validation of both analytical solutions and tools is presented.

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

This paper investigates a general description of a hybrid energy hub (HEH) including multiple energy carriers, such as electricity, hydrogen gas, and heating. The real hybrid energy hub model is based on a conceptual approach for the inclusion of distributed resources. Couplings between the different energy carriers are regarded explicitly, enabling investigations in power and energy flow. The model consists in a smart microgrid with multiple and hybrid energy sources, storages and loads, including electric vehicles charging stations and hydrogen, completely controlled and monitored by a supervisory control and data acquisition platform (SCADA). All the components communicate by an open protocol to the main control for management, analysis and diagnostic. The completely open and flexible structure makes this platform ideal for the conception, simulation and experimental development of innovative systems in the field of microgrids and hybrid energy systems.

The paper proposes an innovative power-sharing model, i.e., a power-system architecture for aggregation of users able to share the power produced by common generators and energy services. The model is suitable for both multi-tenant buildings and groups of multiple buildings and it is applicable for both existing and new buildings. It is scalable for larger systems and suitable for an easier integration with storage systems. The novel principle of the model is that the energy produced by common generators can be shared among the end-users in a unidirectional way, so that each user remains passive towards the distributor, except a single active user that assumes the role of balance node. This key feature allows for easily implementing the model in all the residential and tertiary multi-units buildings in full compliance with national regulations, with the adoption of power sharing contracts as well. This paper discusses the feasibility of the model through a dynamic Matlab/Simulink model, which is used to show its effectiveness in several case studies. The significance of this work consists of approaching the energy sharing in buildings with a completely new strategy, based on an innovative system architecture that can be effectively implemented.

The concept of energy communities is promising to empower alternative ways of organizing and governing energy systems in the next future. Energy communities will provide to end-users democratic, open access to the grid, and collective self-consumption. They will encourage decentralization and promote distributed renewable energy sources applications. Energy communities are widely recognized to be a fundamental step for a sustainable energy transition. The authors present an innovative power-sharing model that can be easily implemented in new and existing multi-apartment buildings. It allows end-users to collect into a democratic energy community and enjoy beneficial ecological effects and economic benefits.

We propose a deep learning approach for multivariate forecasting of energy time series. It is developed by using Long Short-Term Memory deep neural networks so that different related time series, incorporating information of longterm dependencies, can be joined together as a multidimensional input of the deep neural network. The learning scheme can be represented as a stacked LSTM network in which one or more layers are cascaded, feeding their output to the input of the sequent layer. To prove the effectiveness of the approach, it has been tested on real-world problems pertaining to the energy field, where time series prediction is of paramount importance..

In smart grids and microgrids, time series prediction is a fundamental tool for enabling intelligent energy resource management and advanced interactions between heterogeneous agents. In this work, we propose a solution to the energy forecasting problem based on two machine learning techniques: Convolutional Neural Network and Long Short-Term Memory Network. These techniques are combined with a new embedding format to appropriately feed the time series to the stacked network architecture. The resulting novel deep learning scheme is able to retrieve information from the data by inferring time dependent correlation structures. The model is validated using real-world examples, showing good performances with a 3-days forecasting horizon.

The need of reliable prediction algorithms of energy production is increasing due to the spread of smart solution for grid, plant and resource management. Recurrent neural networks are a viable solution for prediction but their performance is somewhat insufficient when the time series is generated by an underlying process that behaves in a complex manner. In this paper, a new combination of echo state network and genetic algorithms is employed in order to improve the prediction accuracy of photovoltaic time series. The genetic algorithm is used to embed past samples of the time series to be used for predicting a new one. It aims at a feature extraction in order to regularize data being fed into a neural network model, so that it is able to learn more robust and generalizable prediction models. The experimental tests prove that the proposed approach is suited to the application focused in this paper.

In the present work we assess the lightning performance against direct strokes of overhead distribution lines, making use of the theory of multiconductor transmission lines with periodical grounding, recently developed by the authors. We assume that the lightning protection is provided by shield wires that are placed both above and below (underbuilt construction) the phase conductors. We adopt the transmission line model to include the propagation effects on the poles. We investigate the effects of geometrical parameters, and, by the analyses of different arrangements, we evaluate the impact on the lightning performance. The core of the work is the demonstration that addition of underbuilt ground wires can be a viable solution for improving the lightning protection of distribution lines.

We investigated 30 kV Ga+ ions treatments on (0001) single crystal ZnO in order to assess the potentiality of ion beam based device fabrication on such material. A multi-technique approach combining atomic force microscopy, Raman and energy dispersive X-Ray spectroscopies, and transmission electron microscopy was used to study morphological and structural properties of ZnO upon varying the ion dose. At low doses a shallow defective layer develops showing an increasing defect density as the dose is increased. At higher dose a thinner defective layer with an amorphous layer on top is produced. The ion beam damaged layer on high resistivity ZnO shows enhanced conductivity. KOH based etching removed selectively the damaged ZnO and was found to dissolve rapidly the Ga-rich amorphous layer. The defective layer has an etch rate which depends on the ion dose, and even for prolonged etching processes it was not completely removed. However, conductivity measurements on ion beam fabricated pillars showed that the residual defects do not give a detectable electrical response. These findings indicate that electronic devices and micro-structures with pristine electrical properties of the ZnO crystal can be reliably fabricated by focused ion beam, provided the damaged surface layer is removed by proper etching procedures. Keywords: Zinc oxide, Focused ion beam, Raman spectroscopy, Transmission electron microscopy, Defects

A novel deep learning approach is proposed for the predictive analysis of trends in energy related time series, in particular those relevant to photovoltaic systems. Aim of the proposed approach is to grasp the trend of the time series, namely, if the series goes up, down or keep stable, instead of predicting the future numerical value. The modeling system is based on Long Short-Term Memory networks, which are a type of recurrent neural network able to extract information in samples located very far from the current one. This new approach has been tested in a real-world case study showing good robustness and accuracy.

Due to its piezoelectric and semiconductive properties, ZnO is actively investigated for the development of innovative nanostructures for applications ranging from piezotronics to energy harvesting. In the present paper we develop a full model for the non-linear piezoelectricity in ZnO nanowires, where both direct and inverse non-linear piezoelectric effects are accounted for. The preliminary results show for the first time the importance of non-linear effects on the electro-mechanic behavior of nanowires especially when used for piezotronic applications.

In the modern scenario of smart-grids, the concept of virtual power plant (VPP) is undoubtedly a cornerstone for the smooth integration of renewable energy sources into existing energy systems with a high penetration level. A VPP is the aggregation of decentralized medium-scale power sources, including photovoltaic and wind power plants, combined heat and power units, as well as demand-responsive loads and storage systems, with a twofold objective. On one hand, VPP relieves the stability and dispatchability problems on the external smart grid since it can be operated on an individual basis, appearing as a single system on the whole. On the other hand, VPP improves flexibility coming from all the networked units and enable traders to enhance forecasting and trading programs of renewable energies. This paper proposes a novel distributed decentralized prediction method for the management of VPPs. The novelty of the proposed technique is to effectively combine the concepts of neural networks and machine learning with a distributed architecture that is suitable for the aggregation purposes of the VPP.

Academic laboratories should be a safe environment in which one can teach, learn, and conduct research. Sharing a common principle, the prevention of potential accidents and imminent injuries is a fundamental goal in the laboratory environments. In addition, academic laboratories are attributed exceptional responsibility to instill in students the culture of the safety, the basis of risk assessment, and of the exemplification of the prudent practices around energized objects. Undergraduate laboratory assignments may normally be framed based upon the repetition of established experiments and procedures, whereas academic research laboratories may involve new methodologies and/or apparatus, for which the hazards may not be completely known to the faculty and student researchers. Yet, the academic laboratory should be an environment free of electrical hazards for both routine experiments and research endeavors, and faculty should offer practical inputs and safety-driven insights to academic administration to achieve such a paramount objective. In this article, the authors discuss the challenges to electrical safety in modern academic laboratories, where users may be exposed to harmful touch voltages.

A novel deep learning approach in proposed in this paper for multivariate prediction of energy time series. It is developed by using Convolutional Neural Network and Long Short-Term Memory models, in such a way that several correlated time series can be joined and filtered together considering the long term dependencies on the whole information. The learning scheme can be viewed as a stacked deep neural network where one or more layers are superposed, feeding their output in the sequent layer’s input. The new approach is applied to real-world problems in energy area to prove robustness and accuracy.

With the aim of tuning the optical properties of SiC selective solar absorbers, we investigate the surface micro/nano-texturing of porous silicon carbide by using two approaches, namely i) the femtosecond laser treatment and ii) the fluorine based reactive ion etching. It is found that both the techniques are capable of inducing significant surface morphology modification inducing a substantial roughening. The surface features produced are found to be tunable by tailoring the main process parameters, i.e. translation speed and pulse repetition rate for laser treatment, and RF-power, gas pressure and gas mixture for the reactive ion etching process.

Academic laboratories should be a safe environment in which one can teach, learn, and conduct research. Sharing a common principle, the prevention of potential accidents and imminent injuries is a fundamental goal of laboratory environments. In addition, academic laboratories are attributed the exceptional responsibility to instill in students the culture of the safety, the basis of risk assessment, and of the exemplification of the prudent practice around energized objects. Undergraduate laboratory assignments may normally be framed based upon the repetition of established experiments and procedures, whereas, academic research laboratories may involve new methodologies and/or apparatus, for which the hazards may not be completely known to the faculty and student researchers. Yet, the academic laboratory should be an environment free of electrical hazards for both routine experiments and research endeavors, and faculty should offer practical inputs and safety-driven insights to academic administration to achieve such a paramount objective. In this paper, the authors discuss the challenges to the electrical safety in modern academic laboratories, where users may be exposed to harmful touch voltages.

During transients involving multiconductor lines, the importance of the ground finite conductivity is well known and various techniques and expressions have been presented in literature for the inclusion of its contribution into the per unit length parameters. The direct time domain approach based on the introduction of the transient parameters and on the numerical solution of the telegrapher's equations demonstrated to be accurate and efficient for the analysis of typical transients. In this letter, the expressions for the ground transient resistance for underground cables, based on the closed-form inverse Laplace transform of the classical Pollaczek expressions (valid for the low-frequency-range), are presented and discussed.

The analysis of the shielding performance is conducted on the basis of the classical IEEE Standard 299, which is typically a local and frequency-domain approach, may be not sufficient for the thorough appraisal of protection levels achievable through actual enclosures in the presence of transient electromagnetic fields. Stemming from a victim point of view, a new, global approach is proposed for the assessment of average and minimum guaranteed shielding performance directly in the time domain, in the presence of either near fields or plane waves. The method is suitable for the analysis of all the typical transient EMI phenomena and recommended for the analysis of protection levels of mission-critical devices or systems. An elementary statistical analysis of performance data is also presented.

Dyadic Green’s functions for an electric dipole source over an infinite periodic metasurface are obtained using homogenized, nonlocal, anisotropic, generalized sheet-transition conditions. The homogenized Green’s functions can efficiently model near-field point source excitation of typical metasurface structures. The Green’s functions can be decomposed into discrete and continuous spectral components, providing physical insight into the wave dynamics. Several different metasurfaces are considered, and the results are validated by comparison with a full-wave array-scanning method, demonstrating computational efficiency of the proposed homogenized Green’s function approach.

The 17th edition of the IEEE International Conference on Environment and Electrical Engineering (EEEIC) and the 1st edition of IEEE Industrial and Commercial Power Systems Europe (I&CPS Europe) held a joint meeting in Milan, Italy from 6 to 9 June 2017.

Forecasting electricity prices is today an essential tool in the day-ahead competitive market. Prediction techniques based on neural and fuzzy neural networks are very promising in terms of prediction performance and model accuracy. In this paper, we investigate the applicability to the electricity market of three well-known approaches, namely Radial Basis Function neural networks, Mixture of Gaussian neural networks and Higher-Order Neuro-Fuzzy Inference System. Through a set of real-world examples we assess the applicability of such methodologies for medium-term energy price projections.

The preformance of finite size planar shields usually adopted in EMC practice against near field extremely low-frequency magnetic sources magnetic sources are here investigated. The analysis is performed numerically with frequency-domain techniques and it is aimed at assessing the influence of shield constitutive parameters and of the source distance on the shielding parameters.

The paper illustrates a numerical procedure for the computation of mutual transient overvoltages transferred among typical grounding systems of wind turbines belonging to the same wind farm. We use a hybrid approach based on the circuit theory and the Method of Moments in the frequency domain. The transient response is obtained by means of the Inverse Fourier Transform efficiently computed trough a double-exponential formula.

Renewable electricity generation has variable and non-dispatchable output that rises several technical, economic and feasibility concerns, calling for energy storage capacity and forecasting techniques to allow the integration of large amounts of variable generation into existing grids. These problems need careful attention in small islands that are not connected to the national transmission grid. In this paper, we present a study for the small Italian island of Ponza on the use of Echo State Networks to forecast real-world energy time series. In particular, the prediction is applied to the PV plant production and to the load of the electric grid of the whole island. The prediction results are then used to relieve the use and the cost of the diesel generation, by optimally managing a Battery Energy Storage System. This forecasting strategy has good performance, proving that Echo State Networks are suited to the focused application.

The classical problem represented by a planar thin conductive shield in the presence of vertical dipole field sources (electric or magnetic) is solved analytically in the time domain via the Cagniard-de Hoop technique. Comparisons with numerical solutions carried out by means of efficient integration algorithms are provided and discussed.

The shielding performance of different buildings in presence of indirect lightning return strokes are investigated directly in the time domain. To this end, a sensitivity analysis is conducted by varying the channel geometrical characteristics and considering three different buildings. The performance is evaluated by adopting the time-domain shielding parameters recently proposed, which appear to be suitable for shielding studies in presence of lightning electromagnetic pulses. The results confirm the usefulness of introducing different parameters to fully characterize the shielding behavior of buildings exposed to lightning electromagnetic fields.

In high voltage transmission lines, shield wires are periodically grounded and can be periodically sectionalized for several purposes. These two practices have important effects on multiconductor transmission lines, since both affect the wave propagation along the line. In this work, the attention is focused on the frequency behavior of the characteristic impedance. Moreover, the effect of wires transposition on the characteristic impedance is also investigated.

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

In high voltage transmission lines, the shield wires are often sectionalized for several reasons. This practice may have considerable effects on power line communications in multiconductor transmission lines. A careful simulation is performed in order to put in evidence the relevant channel features.

Large-scale ground-mounted PV plants, because of their large extend and localization, can be subject to lightning strikes. When a lightning strike falls into the area of the PV plant, the earthing system is affected by the current flowing in the lightning channel and potentially dangerous overvoltages may occur. Thus, the investigation on the performance of the grounding system is of paramount interest for the prediction of the potential threats either for people working in (or animals passing through) the solar farm area and for the installed electric and electronic equipments. The analysis of grounding systems is conducted in the frequency domain and an hybrid approach, based on circuit theory and Method of Moments, is adopted in order to fully account for resistive, inductive and capacitive couplings between elements of the ground system. The actual transient behavior is obtained by means of an Inverse Fourier Transform. A real case study grounding system configuration is considered and analyzed. We show that, even if the requirements of the international standards are fulfilled, and touch and step voltages are controlled with special care, overvoltages may cause failures of internal systems.

The paper describes a measurement campaign conducted at the Engineering Department of Astronautics, Electrical and Energetic, Sapienza University of Rome. Starting from measurement campaign, in the context of building an urban microgrid, a PV plant and a energy storage system has been proposed. The measurements were conducted in two relevant period of the year in order to know the power consumption of the facility. The knowledge of total energy demand by electrical loads allows the identification of most efficient strategies for energy utilization by using the PV production and energy storage.

High penetration level of intermittent and variable renewable electricity generation introduces signicant challenges to energy management of modern smart grids. Solar photo-voltaics and wind energy have uncertain and non-dispatchable output which leads to concerns regarding the technical and economic feasibility of a reliable integration of large amounts of variable generation into electric grids. In this scenario, accurate forecasting of renewable generation outputs is of paramount importance to secure operation of smart grids. In this paper, we present a study on the use of fuzzy neural networks and their application to the prediction of solar photovoltaic outputs. The new learning strategy is suited to any fuzzy inference model. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying unknown process with a good prediction of the observed time series.

The shielding effectiveness of thin planar shields against magnetic field is investigated in the time domain. The analysis is performed considering a vertical magnetic dipole as the source and a classical twelve inches diameter loop as receiving antenna. The computations are performed through a recently proposed analytical solution. The relevant features of classical configurations are extracted and the effects of shield constitutive parameters on the relevant time-domain shielding parameters are presented.

The performance of electromagnetically thin planar shields are investigated in the time domain considering near field sources in order to extract the relevant features of reference configurations. The analysis is performed numerically and the influence of shield constitutive parameters and of the source distance on the relevant time-domain shielding parameters is also presented.

In the last ten years, after the first Italian feed-in-tariff, it has been observed that during the operation and maintenance of large-scale grid-connected photovoltaic systems, several unexpected problems may occur whose solution calls for specific strategies not initially planned at the design stage. Based on information gathered from more than 80 plants located in Italy, this work outlines the most widespread issues, the principal typologies of failures and unexpected events that occur in photovoltaic plants and tries to propose the best ways to solve them.

Different corona models are investigated with reference to the propagation along lossy overhead multiconductor transmission lines of overvoltages due to direct lightning strokes. A nonlinear dynamic capacitance is introduced to simulate the nonlinear behavior of the q-v hysteretic loop and account for the losses associated with its area. For comparison, an equivalent circuit has been introduced to simulate the corona phenomenon through a voltage-dependent current-generator and a nonlinear conductance in parallel. In this paper we compare the predictions of different models in comparison with measured data available in literature. Then, a model based on a nonlinear dynamic capacitance is implemented in an efficient Finite Difference Time Domain (FDTD) method where also the losses in the ground return path are simulated by means of the expression ensuring from Carson theory transferred in the time-domain.

In the modern distributed generation scenario, the installation of domestic-scale energy generation systems has dramatically growth, raising a number of problems on both the prosumer and distribution sides whose solution calls for new concepts of demand-side energy management of smart grids. These new strategies require an improved smartness of the distribution networks, but also encourage customers not only to participate in energy generation but also in an efficient electricity consumption. The widespread penetration of energy storage systems (ESS) at the domestic domestic level could be an important step toward the application of modern strategies which incentivate energy consumption/production scheduling games. In this paper we develop a Matlab model to simulate the behaviour of a prosumer with a domestic-rated photovoltaic (PV) system, a battery ESS and an electric vehicle (EV) charging station. Simulation results confirm the effectiveness of the ESS in reducing the energy exchanged with the grid as well consumer daily electricity charges.

We report on the conductive-atomic force microscopy (C-AFM) study of metallic layers in order to find the most suitable configuration for electrical characterization of individual ZnO micro-pillars fabricated by focused ion beam (FIB). The electrical resistance between the probe tip and both as deposited and FIB processed metal layers (namely, Cr, Ti, Au and Al) has been investigated. Both chromium and titanium evidenced a non homogenous and non ohmic behaviour, non negligible scanning probe induced anodic oxidation associated to electrical measurements, and after FIB milling they exhibited significantly higher tip-sample resistance. Aluminium had generally a more apparent non conductive behaviour. Conversely, gold films showed very good tip-sample conduction properties being less sensitive to FIB processing than the other investigated metals. We found that a reliable C-AFM electrical characterization of ZnO microstructures obtained by FIB machining is feasible by using a combination of metal films as top contact layer. An Au/Ti bilayer on top of ZnO was capable to sustain the FIB fabrication process and to form a suitable ohmic contact to the semiconductor, allowing for reliable C-AFM measurement. To validate the consistency of this approach, we measured the resistance of ZnO micropillars finding a linear dependence on the pillar height, as expected for an ohmic conductor, and evaluated the resistivity of the material. This procedure has the potential to be downscaled to nanometer size structures by a proper choice of metal films type and thickness. (C) 2016 Elsevier B.V. All rights reserved.

The continuity of shield wires is often broken for losses reduction purposes in high voltage transmission lines. The accurate simulation of the effects of this practice on power line communications in multiconductor transmission lines is performed in the time domain by means of a finite-difference method. Losses in wires and ground are accounted for by means of transient per unit length parameters and recursive convolution. The method overcomes most of the usual problems encountered by frequency-domain modal analysis techniques.

The ability to forecast the power produced by renewable energy plants in short and middle terms is a key issue to allow an high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the Trasmission System Operator level, as well as electrical distributors and power system operators level. In this paper we present three techniques based on neural and fuzzy neural networks, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

Propagation and attenuation properties of modes supported by graphene nano-ribbons placed on top of a grounded dielectric substrate are investigated by means of a spectral-domain Method-of-Moments approach together with a dyadic description of the graphene conductivity obtained based on semiclassical and quantum-mechanical arguments. Such a description takes into account the possible presence of static biasing fields or spatial-dispersion effects. The attenuation features are investigated and are compared to those of similar metal structures.

ZnO is receiving a considerable attention for the development of novel cost-effective nanostructures with outstanding functional properties for applications in electronics and energy. In this paper, we investigate the effects of the nonlinear piezoelectricity, that has been recently observed in ZnO nanostructures, on the piezotronic effect of ZnO nanowires (NWs). We insert a physically-based model of the nonlinear direct piezoelectric effect into a fully-coupled thermo-mechanical-electric scheme to study the current-voltage characteristic of ZnO NWs under a purely vertical compressive/tensile strain. Our results show for the first time that the nonlinear piezoelectricity deeply affects the current transport processes inside the NW and the behavior of devices for piezoelectric-piezotronic applications and provides remarkable insights into the underlying physics. © 2002-2012 IEEE.

Dispersion properties of the dominant modes supported by different 2-D graphene-based nanowaveguides are studied by means of an exact approach based on the transverse-resonance technique and using an equivalent-circuit representation of graphene sheets which also takes into account the spatially dispersive nature of the graphene conductivity: it is quantitatively shown that neglecting spatial-dispersion effects can cause errors in the determination of the modal properties of extremely slow surface waves, also well below the terahertz regime. The modal features of graphene nanowaveguides are thus investigated in detail showing their potential in future nanoelectromagnetic and nanophotonic applications.