Francesco Riganti-Fulginei

• Professor
• Full Professor at Roma Tre University

Extensive research has focused on optimizing energy consumption in residential buildings based on indoor thermal conditions. However, modeling the energy and thermal behavior of non-residential buildings presents greater challenges due to their complex geometries and the high computational cost of detailed simulations. Simplifying input variables c...

Photovoltaic (PV) power forecasting is essential for providing accurate data on future power production, ensuring secure power grid operations, and reducing solar energy operation expenses. This research introduces a novel dual-steam hybrid model that uses Bidirectional Long-Short Term Memory (BiLSTM) and Convolutional Neural Networks (CNN) to pred...

Symbolic regression (SR) has emerged as a powerful tool for the characterization of Wireless Power Transfer (WPT) systems, estimating the distance between coils and finding the relationship between frequency and phase so as to find the best frequency to increase the power factor. This study explores the application of SR on both simulated and exper...

This research evaluates the technical and economic aspects of solar photovoltaic (PV) power installations on farmland, utilizing a simulation model in MATLAB to forecast annual system output based on nominal power and meteorological data. This study compares various configurations, including single-sided versus double-sided modules and fixed versus...

This article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. With the growing demand for wireless charging applications such as electric vehicles (EVs), IoT devices, and medical implants, WPT sys...

In this paper, an innovative charging system for autonomous underwater vehicles is introduced. The proposed architecture aims to guarantee high conversion efficiencies even in the presence of seawater. For this purpose, the structure geometry has been designed and optimised to ensure a strong anchorage even in rough sea conditions. This has also be...

This paper presents an analytical approach for calculating the mutual inductance between superconducting pancake coils in Wireless Power Transfer (WPT) systems, leveraging the improved series-form analytical expression derived from [1]. The method addresses the challenges of misalignment and complex coil geometries typical in high-temperature super...

Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV power forecasts are increasingly crucial for managing and controlling integrated energy systems. Over the years, advanced artificial neural network (ANN) models have been proposed to incre...

Electromagnetic compatibility (EMC) is crucial when designing and operating wireless power transfer (WPT) systems for charging Electric Vehicles (EVs). WPT technology allows the transmission of electrical energy from a power source to a device without requiring physical contact. However, it can generate electromagnetic interference (EMI) that could...

Battery state estimation is fundamental to battery management systems (BMSs). An accurate model is needed to describe the dynamic behavior of the battery to evaluate the fundamental quantities, such as the state of charge (SOC) or the state of health (SOH). This paper presents an overview of the most commonly used battery models, the equivalent ele...

Compensation Admittance Load Flow (CALF) is a power flow analysis method that was developed to enhance the sustainability of the power grid. This method has been widely used in power system planning and operation, as it provides an accurate representation of the power system and its behavior under different operating conditions. By providing a more...

Load frequency control (LFC) has recently gained importance due to the increasing integration of wind energy in contemporary power systems. Hence, several power system models, control techniques, and controllers have been developed to improve the efficiency, resilience, flexibility, and economic feasibility of LFC. Critical factors, such as energy...

Cities need to make themselves energy self-sufficient by exploiting renewable sources and, above all, to evaluate the potential and constraints that each city can express by virtue of its own characteristics. This study focused on how the realisation of a renewable energy community could be approached in urbanised contexts. The methodology involved...

In this paper a forecasting method is proposed for the prediction of the generated power in photovoltaic systems. The approach exploits the combination of a virtual irradiance sensing methodology and a neural network forecasting system. The strength of this approach resides in its capability to support forecasting in presence of distributed shading...

An advanced magnetic hysteresis modelling, exploiting the Preisach theory and the neural networks, is applied and discussed for the simulation of the magnetization processes of magnetic components made by laser powder bed fusion. Silicon iron samples with different percentage silicon content are used for the evaluation of the accuracy and reliabili...

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line loss...

A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the energy versus the amplitude of the magnetic induction) of soft ferromagnetic materials at different operating frequencies is proposed herein. Firstly, an innovative Fe-Si magnetic alloy, grade 35H270, is experimentally characterized via an Epstein f...

The purpose of this work is to devise algorithms to reduce the memory consumption of the vector Preisach model in view of its usage in Finite Element analysis. Four algorithms, which all implement a vector Preisach hy steresis model, are presented and critically compared theoretically and by numerical experiments taken on with two materials and thr...

In this work, a sensitivity analysis for the closed-form approach of irradiance sensing through photovoltaic devices is proposed. A lean expression to calculate irradiance on a photovoltaic device, given its operating point, temperature and equivalent circuit model, is proposed. On this expression, the sensitivity towards errors in the measurement...

This work proposes an Artificial Neural Network (ANN) able to provide an accurate forecasting of power produced by photovoltaic (PV) plants. The ANN is customized on the basis of the particular season of the year. An accurate analysis of input variables, i.e., solar irradiance, temperature and air humidity, carried out by means of Pearson Correlati...

A thorough investigation of the 2-D hysteresis processes under arbitrary excitations was carried out for a specimen of innovative Fe-Si magnetic powder material. The vector experimental measurements were first performed via a single disk tester (SDT) apparatus under a controlled magnetic induction field, taking into account circular, elliptic, and...

A computationally efficient and robust neural network-based model to reproduce the hysteresis phenomenon for soft ferromagnetic alloys is here presented, as well as a dedicated procedure to generate a suitable training set from a minimal set of experimental data. Firstly, an accurate experimental verification has been performed for a commercial NGO...

In this paper, a novel algorithm with high computational efficiency is proposed for the filter adaptation in a feedforward active noise control system. The proposed algorithm Zero Forcing Block Adaptive Filter (ZF-BAF) performs filter adaptation on a block-by-block basis in the frequency domain. Filtering is performed in the time domain on a sample...

In this work, a non-destructive, automated procedure to extract the I-V characteristics of individual cells of fully encapsulated photovoltaic (PV) modules is proposed. The approach is able to correctly identify and extract the electrical parameters of underperforming cells, due for example to defects or degradation. The approach uses multiple I-V...

A real time simulation of battery conditions is an essential step in the development of energy harvesting devices. Since it is not possible to have a direct measurement, the battery information, such as the remaining charge, need to be estimated by means of model-based estimation algorithms. Most of the existing models describing battery behaviour,...

This work proposes a multicarrier energy hub system with the objective of minimizing the economy cost and the CO 2 emissions of a residential building without sacrificing the household comfort and increasing the exploitation of renewable energy in daily life. The energy hub combines the electrical grid and natural gas network, a gas boiler, a heat...

Neural Networks (NNs) are frequently applied to Multi Input Multi Output (MIMO) problems, where the amount of data to manage is extremely high and, hence, the computational time required for the training process is too large. Therefore, MIMO problems are often split into Multi Input Single Output (MISO) problems; MISOs are further decomposed into s...

Photovoltaic (PV) power generation is heavily influenced by mismatching conditions, mainly caused by partial or full shading of an array portion. Such a non-uniform irradiation can lead to severe reductions in the power produced; some techniques, such as array reconfiguration or microconverters and microinverters technology are aimed at retrieving...

In this paper the current-voltage characteristics of organic solar cells (OSC) is analyzed in terms of equivalent lumped parameter circuit at different level of insolation. In particular, starting from a circuital model based on a three-diode configuration, a set of formulas is proposed to describe the dependence of lumped circuital parameters on i...

This work describes a sensitivity analysis for the one-diode model used to represent photovoltaic devices. The parameters of the model can be found through an analytic and numeric procedure starting from the datasheet values given by the constructor. From this repeatable and pseud-deterministic approach, a numerical study of the sensitivity of the...

In this work, we report on the modelling of light soaking effect on Ruthenium-based Dye Sensitized Solar cells (DSSCs). Such a phenomenon can be detected when exposing the cells at increasing hours of illumination and produces a reversible performance increase. Starting from the results obtained through the electro-optical characterization of the c...

Determination of solar irradiance is a critical asset to ensure efficient working conditions for a photovoltaic (PV) system. This work analyze the feasibility of assessing solar irradiance on a PV device assuming the knowledge of the device temperature and the voltage/current operating point. This work proposes an approach based on a manipulation o...

In this paper, a switched-capacitor voltage regulator with a very low output ripple and decoupling capability is presented. To implement this regulator, a dual-phase, double fly capacitors configuration is proposed. Besides having a very low output ripple, it is demonstrated how it is very effective in suppressing power input noise as well, for exa...

The single hysteron model is identified to reconstruct the magnetization processes of a grain oriented electrical steel and it is implemented in a finite element scheme. The model involves the Zeeman energy and the anisotropy energy of the material and an interaction field to take into account others terms such as the magneto-elastic energy, the ex...

In this paper a technique based on contactless magnetic measurements is described in order to evaluate the orientation of the crystal grains in grain-oriented ferromagnetic materials. It is shown that both the orientation of the major easy axis respect to the rolling direction and the angle of the out of plane-axes respect to the lamination plane c...

A generalization of the VHM (Vector Hysteron Model) is here described to reconstruct rotational magnetizations of electrical steels with weak anisotropy, like non oriented grain Silicon steel, in function of the frequency values. The VHM is a natural extension of the classical Preisach model where the scalar hysterons are changed with vector hyster...

In this work we propose a computational procedure to simulate the magnetic behavior of inductive blocking devices for the mitigation of the induced voltage and current pulses on the aircrafts circuits during a lightning shock. The non linear and hysteretic phenomenon are simulated using a model based on the Preisach theory and implemented in a fini...

A new hysteresis operator for the simulation of Goss-textured ferromagnets is here defined. The operator is derived from the classic Stoner-Wohlfarth model, where the anisotropy energy is assumed to be cubic instead of uniaxial, in order to reproduce the magnetic behaviour of Goss textured ferromagnetic materials, such as grain-oriented Fe-Si alloy...

A new general typology of optimization algorithm inspired from the classical swarm intelligence, the Continuous Flock-Of-Starlings Optimization (CFSO), is used to face the inverse problem of modeling magnetic materials. It is obtained by translating the numerical swarm/flock-based algorithms into differential equations in the time domain and employ...

In this paper the texture reconstruction of magnetic materials by means of a magnetic model for vector hysteresis is presented. The evaluation of the orientation distribution function (ODF) from magnetic measurements is being carried out for both non-grain-oriented and grain-oriented electrical steels. The estimated ODFS are finally compared to the...

In this paper, an in-depth analysis of the current–voltage characteristics of organic solar cells is performed by introducing a new one-equation model based on a generalised equivalent circuit capable of accurately fitting ideal and non-ideal curves. The model is based on the introduction of a non-linear series resistance term that can be reduced t...

This work documents the research towards the identification of the FEM model of a measurement workbench for vector magnetic fields in ferromagnetic structures. The device under test is a Round Rotational Single Sheet Tester, having as excitation structure the stator of an induction motor. The FEM model of this workbench is characterized, apart from...

Rotational magnetizations of an Ni-Fe alloy are simulated using two different computer modeling approaches, physical and phenomenological. The first one is a model defined using a single hysteron operator based on the Stoner and Wohlfarth theory and the second one is a model based on a suitable system of neural networks. The models are identified a...

the present work documents the research towards the development of an efficient, fast and reliable black-box model to assess the magnetic field at extremely low frequency in a given volume. The approach is based on the implementation of an array of neural networks (aggregated/bootstrapped) trained on suitably conditioned experimental measurements....

This work documents the progress towards the implementation of an embedded solution for muscular forces assessment during cycling activity. The core of the study is the adaptation to a real-time paradigm an inverse biomechanical model. The model is well suited for real-time applications since all the optimization problems are solved through a direc...

This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other...

The aim of this work is to present a new tool for the analysis of magnetic field problems considering 2-D magnetic hysteresis. In particular, this tool makes use of the Finite Element Method to solve the magnetic field problem in real device, and fruitfully exploits a neural network (NN) for the modeling of 2-D magnetic hysteresis of materials. The...

In this paper a measurement equipment designed to trace the I–V characteristics curves of a photovoltaic (PV) panel in a lot of working conditions is proposed. The approach exploits a microcomputer assisted DC-DC converter used as electronic load. The microcomputer also controls the voltage and current measurements process and provides connectivity...

This paper proposes a fast and accurate algorithm for the computation of the six parameters required by the California Energy Commission (CEC) six parameter photovoltaic (PV) module model (CEC6PPVMM). An up-to-date database of module characteristics is very important in both the field of scientific research and industry applications. Currently, add...

This work proposes a model with dynamic parameters based on the classic Jiles-Atherton model for magnetic hysteresis. The goal of this study is to investigate whether the improved model is able to generalize the material behavior correctly when minor loops are involved. Two non-linear optimization techniques are used for parameters identification:...

An improvement of the Jiles-Atherton model by introducing a dynamic dependence of all five parameters is presented. The values of parameters dynamically change as a function of the magnetic field excitation. The proposed model aims to improve the poor performance of the classic model Jiles-Atherton in generalizing both saturated and minor loops of...

A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of...