Search

The paper presents analytical formulas for computation in the time domain of electromagnetic (EM) fields generated by tortuous cloud-to-cloud (CC) lightning channels over a perfectly conducting ground. For the first time, the study was not limited to a horizontal lightning path but was extended to take into account the natural, tortuous geometry of the lightning channel. After the calculation of the step response, a convolution integration was applied for the computation of the fields generated by an arbitrary current source. The produced electric and magnetic fields were then compared with the fields generated by a horizontal channel. The method can be of primary importance to evaluating the hazards for electric and electronic systems of flying aircraft, estimating the voltages induced on overhead transmission lines by CC lightning, and, in general, evaluating the induced effects on sensitive electric and electronic components. Moreover, it may represent a simple, robust, and time-saving tool for estimating important physical parameters that characterize lightning phenomena.

The improvement of the lightning performance of overhead distribution lines, hit by direct strokes, is investigated considering the use of additional shield wires beneath the phase conductors. The frequency-domain theory of multiconductor transmission lines with periodical grounding is adopted permitting a rigorous evaluation of the phase and shield wire currents and of the overvoltages across insulators. These can be minimized by careful positioning of the underbuilt shield wires. A sensitivity analysis on the overvoltages is conducted by varying either the position of the underbuilt shield wires and the grounding impedance.

Railway industry is experiencing a strong need of renewal and smart technology. Hence, advanced approaches are needed to improve efficiency and sustainability of railway operations. In urban contexts, the public railway sector is object of an inevitable process of revamping, which involves expectations of end-users, railway operators, local supply distributors or local authorities. This paper examines an urban railway power system, where a smart scenario is envisaged. Particularly, an Italian AC/DC railway power infrastructure is considered, and an energy efficiency strategy is assumed. Aim of the paper is to evaluate the voltage quality in the supplying distribution network for any operating conditions of the railway system. Also, the energy efficiency strategy is arranged to improve voltage quality. More specifically, the strategy exploits electrical energy storage systems recharged by regenerative braking of rolling stocks in order to increase efficiency of the whole system. Also, the storage system allows shaving the peak load demand aimed at improving the voltage quality. Numerical simulations are carried out, they provide power quality analysis by examining nodal voltages as a function of the efficiency measure. The behavior of the railway power system has been analyzed under different operating conditions.

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.

This work is devoted to the analysis of voltages produced on overhead distribution lines hit by direct lightning. We will consider arbitrary terminations at both line ends and, also, arbitrary positioning of the pole being struck. We will deal with lines protected by shield wires, placed either above or below the phase conductors. The analytical tools chosen to deal with such complex system are the transfer matrix method and the theory of multiconductor transmission lines with periodic grounding. The innovation of an articial symmetrical unit cell has been introduced to eliminate the need of two characteristic wave impedance matrices and to ease computation ow.

Combined heat and power (CHP) generation is a fundamental practice to reduce primary energy consumptions and mitigate the related greenhouse gas emissions. Cogeneration plants are even more important in the Hospital sector, as they give the opportunity to operate regardless of the electric power grid, so ensuring energy availability also during grid faults while providing significant cost savings to the public healthcare system. The integration of a battery energy storage with the CHP system could further increase the expected advantages by enabling more flexible operating strategies, further energy reliability and lower operating costs. Therefore, this research paper addresses the development of a specific methodology for the energetic and economic assessment of a grid connected CHP plant assisted by a battery energy storage. More specifically, an evaluation algorithm has been developed and then coupled to a genetic evolutionary algorithm. Then, a vector optimization problem has been solved to find the optimal configurations of a modular cogeneration plant (i.e. size and number of CHP gas engines; battery size) which maximize the primary energy saving while minimizing the payback period. Results based on electricity and heat demand of a hospital facility demonstrate how the battery energy storage system allows the shifting of the Pareto frontier towards better economic results if compared with Pareto solutions found when the optimization problem does not consider a battery storage supporting the CHP plant. The proposed methodology provides an effective and flexible procedure for the optimal configuration and detailed analysis of CHP plants integrated by an electrochemical based energy storage system. In fact, the effects of a high number of interacting energetic and economic parameters are considered together with the technical constraints required to extend battery lifetime in real applications.

The problem of the shielding evaluation of an infinitesimally thin perfectly conducting circular disk against a vertical magnetic dipole is here addressed. The problem is reduced to a set of dual integral equations and solved in an exact form through the application of the Galerkin method in the Hankel transform domain. It is shown that a second-kind Fredholm infinite matrix-operator equation can be obtained by selecting a complete set of orthogonal eigenfunctions of the static part of the integral operator as expansion basis. A static solution is finally extracted in a closed form which is shown to be accurate up to remarkably high frequencies.

Power-forecasting model is a relevant topic in modern power systems. This is due to the wide integration of Renewable Energy Sources (RESs) and their not predictable production. To face this issue, the paper presents a comparison between the performances of the traditional Autoregressive (AR) statistical method and the more recent Artificial Neural Network (ANN) method. The comparison is applied in forecasting of the most significant RES present in the Sicily zone (Italy), i.e., solar sources. To this aim, using the data of Gestore Servizi Energetici (GSE), both the AR and ANN architectures are built by exploiting the information available in time period 2010 - 2015 in terms of temperatures and solar irradiation, thus obtaining a power forecasting for 2016 for each country of the Sicily zone. The performances of the two different methods are compared on the basis of both Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. These two indexes are used to evaluate the accuracy of the proposed models.

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.

Typically, in partial discharges (PD) diagnostics tests carried out in a laboratory, harmonic content in a voltage waveform is ignored. However, during the operation of variable-frequency drives (VFD)-fed electric motors, the distorted voltage waveform is produced due to the addition of harmonics components in the sinusoidal voltage waveform. The distortion level increases when the VFD is operated at a lower speed ratio. The additional spectral components in the applied voltage waveform can have a significant impact on the PD behavior in the insulation of the electric motor. Therefore, during online PD monitoring, overlooking the harmonic regime in the applied voltage may lead to a false interpretation of PD data. This article investigates the influence of voltage harmonics distortion produced during the variable-speed operation of VFD on PD characteristics. For this purpose, online PD measurements have been carried out by performing a series of experiments on eight VFD-fed motors under variable operating conditions. The PD severity at different harmonic compositions is experimentally investigated by determining various PD characteristic parameters including PD inception voltage, accumulated apparent charge, average discharge current, discharge power, and quadratic rate. Also, two terms Gaussian models have been mathematically developed using 288 data samples to quantify the variations in PD characteristic parameters against distortion parameters. The article proposes a framework for properly evaluating harmonics impact in PD for electric motors and correctly estimating stator insulation degradation.

In this paper we consider the problem of restoring the voltage for stand-alone inverter-based Microgrids despite the effects of the time-delays arising with the information exchange among the electrical busses. To guarantee that all Distributed Generators (DGs) reach in a finite-time and maintain the voltage set-point, as imposed by a virtual DG acting as a leader, we suggest a novel robust networked-based control protocol that is also able to counteract both the time-varying communication delays and natural fluctuations caused by the primary controllers. The finite-time stability of the whole Microgrid is analytically proven by exploiting Lyapunov-Krasovskii theory and finite-time stability mathematical tools. In so doing, delay-dependent stability conditions are derived as a set of Linear Matrix Inequalities (LMIs), whose solution allows the proper tuning of the control gains such that the control objective is achieved with required transient and steady-state performances. A thorough numerical analysis is carried out on the IEEE 14-bus test system. Simulation results corroborate the analytical derivation and reveal both the effectiveness and the robustness of the suggested controller in ensuring the voltage restoration in finite-time in spite of the effects of time-varying communication delays.

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.

We study the shielding performance of shield wires in overhead power lines. We propose a novel chain matrix formulation, that employs longitudinally symmetric transmission-line cells to more efficiently, and correctly, simulate the propagation behaviour over the line accounting for the periodical grounding. The analysis is performed in the frequency domain and results in time domain are obtained through inverse Fourier transform. In the case of direct lightning, we can easily compute overvoltages across insulator strings for insulation coordination purposes.

Surge arresters may represent an efficient choice for limiting lightning surge effects, significantly reducing the outage rate of power lines. The present work firstly presents an efficient numerical approach suitable for insulation coordination studies based on an implicit Crank–Nicolson finite difference time domain method; then, the IEEE recommended surge arrester model is reviewed and implemented by means of a local implicit scheme, based on a set of non-linear equations, that are recast in a suitable form for efficient solution. The model is proven to ensure robustness and second-order accuracy. The implementation of the arrester model in the implicit Crank–Nicolson scheme represents the added value brought by the present study. Indeed, its preserved stability for larger time steps allows reducing running time by more than 60 % compared to the well-known finite difference time domain method based on the explicit leap-frog scheme. The reduced computation time allows faster repeated solutions, which need to be looked for on assessing the lightning performance (randomly changing, parameters such as peak current, rise time, tail time, location of the vertical leader channel, phase conductor voltages, footing resistance, insulator strength, etc. would need to be changed thousands of times).

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 large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable, and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper outlines the potential role of Knowledge Discovery from massive Datasets in smart grid computing, presenting the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee.

The probability of flashovers due to indirect lightning on overhead lines is us ually calculated assuming a statistical distribution for the induced voltages, and a deterministic and specified value for the insulation level of the line. However, more accurate practices of insulation coordination suggest a statistical treatment for the insulation level too, in order to relate the probabilistic nature of both electrical stress (induced voltage) and the electrical strength (insulation level). In this paper, a statistical insulation coordination method is applied for the assessment of induced-voltage flashovers and, more in general, for the indirect lightning performance of the line. Such a statistical approach, which is inherently more accurate than the traditional approach, allows also to discriminate among probability of occurrence of flashovers on one, two, or on all phases.

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.

In this paper, we thoroughly review all analytical solutions (both exact and approximate) presented to date in the literature for evaluation of voltages induced on a lossless horizontal conductor, placed over an infinite-conductivity ground, and excited by an external field produced by either a step function or a linearly rising current, moving unattenuated and undistorted along a vertical lightning channel. We also obtain the exact analytical solution for the step-function current case in the time domain via development in the s -domain. To date, there had been three attempts to obtain such solution, all of which yielding incorrect results.

In this paper, we present a new solution for the evaluation of the horizontal electric field in the presence of finitely conducting ground in the time domain. The solution is general, numerically robust, and overcomes the problems related to previously proposed approaches.

This paper deals with the use of distributed energy storage systems in microgrids, and proposes a planning method which accounts for the uncertainties of load and distributed generation. Objectives of the planning method are the reduction of the energy costs, while providing the supply of ancillary services as a technical support to the network. The energy costs are evaluated considering a hourly varying pricing scheme and optimizing the storage systems charging/discharging stages. The technical support is devoted to the restraint of bus voltage amplitudes, and of network components' currents/powers within admissible ranges. The input data uncertainties are managed through three decision theory criteria (i.e., the minimization of expected costs; an approach based on the weighted regret felt by the design engineer; and a stability area criterion), which allow considering the multiple design alternatives and futures (i.e., possible values of uncertain input data) in an accurate and feasible way. The design alternatives refer to the size and location of the distributed storage systems, while each future is associated with a different level of load demand and power production of distributed generation over the whole planning period. The results of numerical applications are reported and discussed with reference to a Cigré test network.

In this paper the results of the estimated electric field associated with tortuous lightning paths at close distance (50 m to 500 m) are shown. Such results are compared with experimental data available in the literature and are illustrated along with a quantitative analysis of the field waveforms and their frequency spectra. The limits of the usual straight-vertical channel assumption and the influence of tortuosity at different azimuth and distances from the lightning channel base are also highlighted.

Modern power distribution systems require reliable, self-organizing and highly scalable voltage control systems, which should be able to promptly compensate the voltage fluctuations induced by intermittent and non-programmable generators. However, their deployment in realistic operation scenarios is still an open issue due, for example, to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, time-delays in data acquisition and transmission are unavoidable and their effects have to be taken into account in the control design phase. To this aim, in this paper, we propose a fully distributed cooperative control protocol allowing the voltage control to be achieved despite the presence of heterogeneous time-varying latencies. The idea is to exploit the distributed intelligence along the network, so that it is possible to bring out an optimal global behavior via cooperative distributed control action that leverages both local and the outdated information shared among the devices within the power network. Detailed simulation results obtained on the realistic case study of the IEEE 30-bus test system are presented and discussed in order to prove the effectiveness of the proposed approach in the task of solving complex voltage control problems. Finally, a robustness analysis with respect to both loads variations and hard communication delays was also carried to disclose the efficiency of the approach.

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.