1,720,995 research outputs found
Characterization of electromagnetic device by means of spice models
Full text linkIn this paper, the lumped parameter circuital approach devoted to the simulation of massive, conductive, and ferromagnetic cores including eddy currents and non-linearity is presented. In the first part of the paper, the circuit analogies devoted to the simulation of magnetic structure coupled with external electrical and eventually mechanical equations are summarised. The two techniques are known in the literature as reluctance-resistance and permeancecapacitance analogies. In particular, it is put in evidence the exploitation of the gyrator component in the modelling of the coupling among magnetic and electrical quantities. The originality of this paper consists in demonstrating for the first time that the rotator-capacitor approach is very suitable for simulations in spice environment and the solution is validated on real applications. Following the circuital approach, the effect of the conductivity and nonlinear magnetic behaviour of the magnetic branches is formalized and introduced in the model. The simulation of the conductivity behaviour, which introduces in massive cores significant eddy current effects, is modelled according to the two possible analogies: the reluctance and the permeance-capacitor model. Under sinusoidal steady-state behaviour, energy aspects related to the two models are then presented and discussed. The non-linearity is taken into account through the fixed-point technique which is suitable for a lumped circuit representation. The full circuital approach is then adopted for the simulation of the real electromechanical actuator under transient and sinusoidal steady-state behaviour conditions. The simulated result is then compared with numerical finite element and experimental results
Flywheel Energy Storage System in Italian Regional Transport Railways: A Case Study
No full textIn this paper, we looked at the role of electromechanical storage in railway applications. A mathematical model of a running train was interfaced with real products on the electromechanical storage market supposed to be installed at the substation. Through this simulation, we gathered data on the recoverable energy of the system, its advantages, and its limitations. Various storage powers were run along variations in speed and gradient to paint a clearer picture of this application. Throughout these simulations, the energy savings were between 25% and 38%, saving up to 0.042 kWh/(seat km)
Recent results on the use of Artificial Intelligence techniques applied to Wireless Power Transfer systems
No full textThis 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 systems face challenges in terms of coil alignment, foreign object detection, and power efficiency. The use of ML algorithms, particularly neural networks and reinforcement learning has emerged as a promising solution to address these challenges. We explore how ML can optimize the geometric and structural design of WPT coils, predict the optimal parameters for inductive couplers, and enhance coupling efficiency under varying conditions. Additionally, ML is being used for foreign object detection (FOD) to ensure safety by identifying metallic and living objects that may interfere with power transmission. The article discusses various approaches, including supervised learning, regression models, and Q-learning algorithms, highlighting their ability to reduce design time, improving system efficiency, and mitigate risks associated with misalignment and object interference. By reviewing recent advancements and ongoing research, this paper provides a comprehensive overview of the potential and limitations of integrating ML into WPT systems, paving the way for smarter, safer, and more efficient wireless charging technologies
Characterization of the shielding properties of a power transformer enclosure
No full textThe aim of this work is to present a novel and compact magnetic field source designed to test the performances of electromagnetic shields. To date, there is no well-defined procedure to evaluate the shielding capabilities of these systems. For this reason, a method is proposed in this paper in which an artificial source generate a magnetic field in all three dimensions, to evaluate the shielding factor (SF) of the enclosure of a 630 kVA cast resin transformer. This system compared to mono or bi-axial sources allows to better evaluate the SF of the object under examination. The triaxial coil source was made using the fused deposition modeling (FDM) technology using PLA material for the supports. This allows for a lightweight and portable object to be made
Advanced Feature Analysis of Eddy Current Testing Signals for Rail Surface Defect Characterization
Full text linkThe maintenance of the railways is of paramount importance for safe and reliable transport.
Eddy Current Testing (ECT) provides high-resolution time-series signals that capture subtle anomalies on
the rail surface. This paper expands on previous analyses by combining classical time-frequency methods
(short-time Fourier transform and continuous wavelet transform) and estimation of fractal dimensions
with advanced feature extraction approaches, including wavelet sub-band decomposition, Hilbert–Huang
transform, peak analysis and entropy metrics. Subsequently, a Random Forest classifier is applied to each
set of characteristics, and we report comparative accuracy results on a dataset comprising rail segments with
joints, welds, or squats. Experimental findings reveal that the Hilbert–Huang transform features yield the
highest accuracy (93.28 %), while simpler features, such as peak counts, are less discriminative (46.93 %).
These results underscore the effectiveness of using multiple signal-decomposition strategies and advanced
analytics to robustly detect and categorize surface defects for better rail-maintenance decision
Passive loop optimisation for HV Joint zone
No full textThe limits of magnetic flux densities over buried High Voltage cables require, in some cases, means to shield these fields; a method widely used in the industry is the application of passive loops. Short-circuited passive loops represent a subset of passive loops and mainly differ in the connection between the cable terminations. A numerical optimization approach has been carried out to optimize the shielding capabilities of passive loops over HV cable junction zones. The mathematical model, the topological layout, and the effects of shielding wire size and numbers have been studied, and the results presented. A comparison with concentric passive loops and HMCPL shielding has been conducted, highlighting the advantages and disadvantages of this apparatus
Compensation Admittance Load Flow: A Computational Tool for the Sustainability of the Electrical Grid
Full text linkCompensation 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 accurate representation of the power system, it can help identify potential problems and improve the overall performance of the grid. This paper proposes a new approach to the load flow (LF) problem by introducing a linear and iterative method of solving LF equations. The aim is to obtain fast results for calculating nodal voltages while maintaining high accuracy. The proposed CALF method is fast and accurate and is suitable for the iterative calculations required by large energy utilities to solve the problem of quantifying the maximum grid acceptance capacity of new energy from renewable sources and new loads, known as hosting capacity (HC) and load capacity (LC), respectively. Speed and accuracy are achieved through a properly designed linearization of the optimization problem, which introduces the concept of compensation admittance at the node. The proposed method was validated by comparing the results obtained with those coming from state-of-the-art methods
Artificial Neural Networks for Photovoltaic Power Forecasting: A Review of Five Promising Models
No full textSolar 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 increase the accuracy of PV power forecasts for various geographical regions. Hence, this paper provides a state-of-the-art review of the five most popular and advanced ANN models for PV power forecasting. These include multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). First, the internal structure and operation of these models are studied. It is then followed by a brief discussion of the main factors affecting their forecasting accuracy, including forecasting horizons, meteorological conditions, and evaluation metrics. Next, an in-depth and separate analysis of standalone and hybrid models is provided. It has been determined that bidirectional GRU and LSTM, whether used as a standalone model or in a hybrid configuration, offer greater forecasting accuracy. Furthermore, hybrid and upgraded metaheuristic algorithms have demonstrated exceptional performance when applied to standalone and hybrid ANN models. Finally, this study discusses various limitations and shortcomings that may influence the practical implementation of PV power forecasting
Measuring thermal and electrical performances of additively manufactured magnetic shielding material: an active thermography approach
No full textThe thermal and electrical responses of additive manufactured specimens were analysed for a additive manufactured steel magnetic shield as a case study. The analysis was based on the evidence that variations in the thermal properties of a material can be measured as a phase delay in thermal diffusion through the material bulk. The signal post-processing was performed, and the results were presented in a phase diagram. The results showed that after heat treatment, the slope of the phase diagram changed to less steep, indicating an increase in thermal diffusivity and hence thermal conductivity. The electrical conductivity was predicted using the thermal conductivity and the Weidemann-Franz law and validated by experimental measurements of the electrical conductivity. The same approach was applied to predict the electrical conductivity in the magnetic shielding, taking into consideration the scaling of the density due to porosity. The results showed that the thermographic non-destructive full field non-contact approach can be used to evaluate the electrical properties of a component and that the heat-treated specimens show better thermal diffusivity and hence thermal and electrical conductivity
A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems
No full textBattery 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 electrical circuits, and data-driven ones, discussing the importance of battery modeling and the various approaches used to model lithium batteries. In particular, it provides a detailed analysis of the electrical circuit models commonly used for lithium batteries, including equivalent circuit and thermal models. Furthermore, a comprehensive overview of data-driven approaches is presented. The advantages and limitations of each type of model are discussed. Finally, the paper concludes with a discussion of current research trends and future directions in the field of battery modeling
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