Mario Cacciato

This paper presents an analytical modeling approach for silicon carbide (SiC) power MOSFETs, focusing on optimizing parasitic parameters to achieve high accuracy with experimental results. Power device modeling combines state-space equations with manufacturer datasheet data to develop a computationally efficient equivalent electrical circuit model that accurately replicates switching behavior. An optimization algorithm refines parasitic parameters, ensuring a strong correlation with experimental data. Validated through MATLAB/Simulink simulations and experimental comparisons, the proposed model demonstrates superior accuracy and significantly reduced computational times relative to traditional SPICE models.

The main aim of this work is to present a step-by-step procedure to model and analyze the power loss distribution of three-level Gallium Nitride (GaN) inverters. It has been applied to three distinct three-phase three-level voltage source inverters utilized in electric traction drives: Active Neutral Point Clamped, Neutral Point Clamped and T-Type Neutral Point Clamped. The proposed analytical power loss modelling, combined with an equivalent representation of the electrical machine proved to be a viable solution to achieve a time-saving and low computational burden simulation platform, leading to satisfying accurate results. This has been confirmed by comparing the results carried out from the simulations of a 110 kW permanent magnet synchronous motor drive and those determined by considering a simplified circuital representation based on the proposed analytical power loss modelling.

The main aim of this work is to present a step-by-step procedure to model and analyze the power loss distribution of three-level Gallium Nitride (GaN) inverters. It has been applied to three distinct three-phase three-levels voltage source inverters utilized in electric traction drives: Active Neutral Point Clamped, Neutral Point Clamped and T-Type Neutral Point Clamped. The proposed analytical power loss modelling, combined to an equivalent representation of the electrical machine demonstrated to be a viable solution to achieve a time-saving and low computational burden simulation platform, leading to satisfying accuracy results. This has been confirmed by comparing the results carried out from the simulations of a 110kW permanent magnet synchronous motor drive and those determined by considering a simplified circuital representation based on the proposed analytical power loss modelling.

The temperature of the photovoltaic modules influences the electrical power output, as the temperature increases, the power extracted will decrease, other environmental conditions being equal. The degradation of PV modules over time is also influenced by the operating temperature and thermal cycling experienced by the modules. The estimation of the module temperature is therefore of crucial importance if it is not measurable due to the absence of sensors or for preliminary studies. In the present work, conventional models with machine learning regressors are compared for the estimation of the temperature of monofacial and bifacial photovoltaic modules installed in two different locations.

It is well known that photovoltaic modules have a non-linear degradation trend which depends on the technology of the modules, on the manufacturer and on the place of installation. It is possible to use specific tools suitable for measuring the I-V curves and apply standards such as IEC 60891: 2021 to evaluate the degradation. However, in the absence of specific tools it is possible, even with greater uncertainty of the results, to estimate the degradation rate of the PV modules. One of the indices that can be used, is the Performance Loss Ratio (PLR). This index, through filtering and data processing procedures, allows to evaluate the loss of performance including both reversible and irreversible effects. This study deals with the evaluation of the loss of performance considering the seasonality effect, that is here described using Fourier series. Filtered data from a string of PERC monocrystalline silicon bifacial modules are used for the work. The usefulness of the method is to evaluate the seasonality effect on the early-stage degradation trend using different metrics, computing a dataset of electrical and environmental variables and without the aid of external tools.

The degradation trend of photovoltaic modules depends on the technology, manufacturing techniques and climatic conditions of the site where they are installed. Longer useful life of the PV modules means that they will be able to produce much more energy than was used to build them; thus, extending the useful life of the modules is beneficial for the environment and increases the cost effectiveness of PVs. The problem of land use has prompted the development of agrivoltaic systems to exploit the same land both for the production of energy and for agriculture, and on water surfaces such as lakes and dams (floating PV). The exploitation of floating PV systems in onshore and offshore areas is currently under study. This constitutes an opportunity for which many factors must be taken into account; a fundamental aspect is the environmental impact, on which some recent studies have focused. Another aspect is the impact of the marine environment on PV system reliability and durability, due to the stress on operating conditions. The aim of this preliminary study is to evaluate the influence of the marine environment on the degradation trend of photovoltaic modules, based on existing models whose inputs are meteorological data from offshore locations. The results obtained from the application of a cumulative exposure model unexpectedly showed a lower degradation value in the offshore environment than on the mainland: −0.95% and −3% values of power decay, respectively. The absolute value of power decay in the onshore case is higher than the typical values because the used model has to be revised, as the empirical coefficients of the model have to be calculated according to the installation environment. The empirical coefficients used in the model were obtained in environmental conditions different from those under study. In the offshore case, the degradation estimated by the model does not take into account some environmental factors typical of the marine environment. Model adaptations calibrated with datasets of plants in environmental conditions similar to those analyzed would allow for greater accuracy in the results.

Considering the importance of lithium-ion (Li-ion) batteries and the attention that the study of their degradation deserves, this work provides a review of the most important battery state of health (SOH) estimation methods. The different approaches proposed in the literature were analyzed, highlighting theoretical aspects, strengths, weaknesses and performance indices. In particular, three main categories were identified: experimental methods that include electrochemical impedance spectroscopy (EIS) and incremental capacity analysis (ICA), model-based methods that exploit equivalent electric circuit models (ECMs) and aging models (AMs) and, finally, data-driven approaches ranging from neural networks (NNs) to support vector regression (SVR). This work aims to depict a complete picture of the available techniques for SOH estimation, comparing the results obtained for different engineering applications.

Measuring the gate-source voltages of wide bandgap (WBG) devices in half-bridge converters provides useful information about susceptibility to crosstalk, which can cause uncontrolled cross-conduction and excessive gate-oxide stresses. However, parasitic elements of the printed circuit board (PCB) and device packaging can compromise measurement accuracy, causing unreliable results. Here we describe how this shortcoming can be addressed using devices in packages that have a Kelvin source pin, allowing a more accurate analysis.

The traditional approach to calculate the active and reactive power in AC power systems requires the measurement of the phase shift between the voltage and current for the evaluation of the power factor. To do this, power analyzers can implement several methods. In principle, it is always necessary to identify specific points of waveforms (e.g., using a zero-crossing detection technique) and get their time shift. In a similar way, the frequency value must be evaluated in order to calculate the angular frequency. Unfortunately, this kind of common method exhibits some issues, such as the large sensitivity to noise. Moreover, inaccuracies in the evaluation of the power factor have a big impact on the final estimation of the electric power. This paper presents a simple but effective way to calculate the electric power, overcoming the need for a direct measurement of the phase shift and frequency. In particular, it is shown that the active power can be easily calculated as the difference between the peak value of the instantaneous power and apparent power. The reactive power and power factor are evaluated by exploiting the same quantities. The practical implementation of the proposed formulation in power analyzers guarantees several benefits without reducing accuracy.