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In the context of inductive power transfer (IPT) for electric vehicle (EV) charging, the precise determination of the mutual inductance between the magnetic pads is of critical importance. The value of this inductance varies depending on the EV positioning, affecting the power transfer capability. Therefore, the precise determination of its value yields various advantages, particularly by contributing to the optimization of the charging process of the EV batteries, since it offers the possibility of adjusting the position of the vehicle depending on the level of misalignment. Within this framework, algorithms grounded in artificial intelligence (AI) techniques emerge as promising solutions. This research work revolves around the estimation of the mutual inductance in a wireless inductive power transfer system using a resonant converter topology, implemented in MATLAB/Simulink® R2021b. The system output was developed to emulate the behavior of a battery charger. To estimate this parameter, an artificial neural network (ANN) was developed. Given the characteristics of the system, the features were chosen in a way that they could provide a clear indication to the ANN if the vehicle position changed, independently of the charging power. In the pursuit of creating a robust AI model, the training dataset contained approximately 1% of the available data. Upon the analysis of the results, it was verified that the largest estimation error observed was around 3%, occurring at the lowest charging power considered. Hence, it can be inferred that the proposed ANN exhibits the capability to accurately estimate the value of mutual inductance in this type of system. [ABSTRACT FROM AUTHOR]

The operational reliability and high efficiency of modern electrical machines depend on the ability to transfer heat in the construction parts of the machine. Therefore, many authors study various thermal models and work on the development of effective heat dissipation. New insights and methods lead to improved techniques for the thermal design of electrical machines. This paper presents an experimentally validated thermal model of a permanent magnet synchronous motor (PMSM) with an improved slot winding model. It also deals with various approaches to homogenization and equivalent material properties of a tooth-coil winding sub-model. First, an algorithm for building a lumped-parameter thermal network (LPTN) of PMSM is described and its properties and problems are discussed. Subsequently, a sub-model of a slot with a winding based on the finite element method (FEM) is introduced. This sub-model is able to generate different conductor distributions based on probabilistic methods for a specified fill factor. This allows the verification of various homogenization approaches and at the same time it is a tool that automatically calculates thermal resistances for the LPTN. [ABSTRACT FROM AUTHOR]

Uninterruptible Power Supplies (UPS) have been demonstrated to be the key technology in feeding either single- and three-phase loads in a wide range of critical applications, such as high-tier datacenters and medical facilities. To increase the overall system power capacity and resilience, UPS systems are usually connected in parallel. When UPS systems are parallel connected, a circulating current can rise, inhibiting correct system operation. Moreover, having a controlled load power distribution is another fundamental requirement in paralleled UPS systems. However, strategies to ensure these two topics have not been explored to date for UPS systems with a load-side neutral connection. This paper proposes an innovative Finite Control Set Model Predictive Control (FCS-MPC) strategy that ensures circulating current elimination and controlled load power distribution for paralleled UPS systems that use an additional inverter leg for load neutral point connection. Additionally, a system topology based on two parallel-connected UPS systems that can simultaneously supply single- and three-phase critical loads is proposed. Experimental results show the effectiveness and robustness of the proposed control techniques even when different types of loads are connected to the UPS systems. [ABSTRACT FROM AUTHOR]

The current transformers with split-core are used for installation in places where it is impossible to install classic current transformers. Moreover, this design allows for any measurement location change, and even switching one current transformer into several different shapes of bars or cables. Power network operators, striving for more accurate current measurements, require producers to provide current transformers with a special accuracy class 0.2S. Therefore, manufacturers and designers try to meet the market requirements and, similarly to non-demountable current transformers, i.e., with a toroidal core, design current transformers with split-core class 0.2S. To meet the high metrological requirements, 3D analyses of electromagnetic fields were performed, taking into account physical phenomena and not approximate analytical models. Two types of cores and four different arrangements of the secondary windings of the measuring current transformers were considered. The magnetic field distributions, current error, and phase displacement diagrams of all current transformer models were analyzed, and the model of the transformer structure with the best accuracy was selected. Computations were conducted based on the finite element numerical method, and the results were compared with the real model tests. [ABSTRACT FROM AUTHOR]

In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers' interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system's capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure. [ABSTRACT FROM AUTHOR]

This article presents a comparative analysis for the design considerations for a solar power generation transformer. One of the main existing problems in transformer manufacturing is in the renewable energy field, specifically the solar power generation, where the transformer connected to the inverter is operated under a certain harmonic content and operating conditions. The operating conditions of the transformer connected to the inverter are particularly unknown for each solar power plant; thus, the transformer will be subject to a particular harmonic content, which is defined by the inverter of the solar power plant. First, the fundamental calculations for solar power plant transformer and the proposed methodology for the design calculation of the distribution pad-mounted three phase transformer are presented. Then, a design study case is described where a distribution transformer and an inverter of a particular solar power plant are used for the analysis. Next, the transformer under analysis is modeled using finite element analysis in ANSYS Maxwell® software, where the transformer will be designed for a non-harmonic and harmonic content application. Lastly, the main design parameters, flux density, the core losses and the winding excitation voltage of the transformer are calculated and presented in results and discussion section. [ABSTRACT FROM AUTHOR]

This article presents a high-performance lead-free piezoelectric energy harvester (LPEH) system for magnetic field. It based on a Ba0.85Ca0.15Ti0.90Zr0.10O3 + CuO 0.3 wt% (BCTZC0.3) composite was fabricated by sintering at 1450 °C. The BCTZC0.3 composite, which has an enhanced high energy conversion constant ( d 33 × g 33 ), shows improved piezoelectric power-generation performance when compared with conventional piezoelectric energy harvesters. The BCTZC0.3-based LPEH produces instantaneous maximum power of 8.2 mW and an energy density of 107.9 mW/cm3 in a weak magnetic field of 250 μT. This system can be used to charge a capacitor and operate a wireless sensor network (WSN) system to provide temperature sensing and radio-frequency (RF) transmission in a 250 μT magnetic field. The proposed LPEH is a promising green-energy device for potentially self-powering WSN systems when applied. [ABSTRACT FROM AUTHOR]

Power transformers play an important role in electrical systems; being considered the core of electric power transmissions and distribution networks, the owners and users of these assets are increasingly concerned with adopting reliable, automated, and non-invasive techniques to monitor and diagnose their operating conditions. Thus, monitoring the conditions of power transformers has evolved, in the sense that a complete characterization of the conditions of oil–paper insulation can be achieved through dissolved gas analysis (DGA) and furan compounds analysis, since these analyses provide a lot of information about the phenomena that occur in power transformers. The Duval triangles and pentagons methods can be used with a high percentage of correct predictions compared to the known classical methods (key gases, International Electrotechnical Commission (IEC), Rogers, Doernenburg ratios), because, in addition to the six types of basic faults, they also identify four sub-types of thermal faults that provide important additional information for the appropriate corrective actions to be applied to the transformers. A new approach is presented based on the complementarity between the analysis of the gases dissolved in the transformer oil and the analysis of furan compounds, for the identification of the different faults, especially when there are multiple faults, by extending the diagnosis of the operating conditions of the power transformers, in terms of paper degradation. The implemented software system based on artificial neural networks was tested and validated in practice, with good results. [ABSTRACT FROM AUTHOR]

Nowadays, uninterruptible power supplies (UPS) play an important role in feeding critical loads in the electric power systems such as data centers or large communication hubs. Due to the increasing power of these loads and frequent need for expansion or redundancy, UPS systems are frequently connected in parallel. However, when UPS systems are parallel-connected, two fundamental requirements must be verified: potential circulating currents between the systems must be eliminated and the load power must be distributed between the systems according to UPS systems availability. Moreover, a high-quality load voltage waveform must be permanently ensured. In this paper innovative control strategies are proposed for paralleled UPS systems based on Finite Control Set Model Predictive Control (FCS-MPC). The proposed strategies simultaneously provide: controlled load power distribution, circulating current suppression and a high-quality load voltage waveform. A new dynamic converters deactivation mechanism is proposed. This new technique provides improved overall system efficiency and reduced power switches stress. In this paper, two multilevel based UPS systems are parallel-connected. Each UPS contains two three-level Neutral Point-Clamped-Converters (3LNPC) and a three-level DC-DC converter. The presented experimental results demonstrate the effectiveness of the proposed control strategies in several operating conditions. [ABSTRACT FROM AUTHOR]

Recently, the control of multiphase electric drives has been a hot research topic due to the advantages of multiphase machines, namely the reduced phase ratings, improved fault tolerance and lesser torque harmonics. Finite control set model predictive control (FCS-MPC) is one of the most promising high performance control strategies due to its good dynamic behaviour and flexibility in the definition of control objectives. Although several FCS-MPC strategies have already been proposed for multiphase drives, a comparative study that assembles all these strategies in a single reference is still missing. Hence, this paper aims to provide an overview and a critical comparison of all available FCS-MPC techniques for electric drives based on six-phase machines, focusing mainly on predictive current control (PCC) and predictive torque control (PTC) strategies. The performance of an asymmetrical six-phase permanent magnet synchronous machine is compared side-by-side for a total of thirteen PCC and five PTC strategies, with the aid of simulation and experimental results. Finally, in order to determine the best and the worst performing control strategies, each strategy is evaluated according to distinct features, such as ease of implementation, minimization of current harmonics, tuning requirements, computational burden, among others. [ABSTRACT FROM AUTHOR]