Hasil untuk "Applications of electric power"

Devendra Patil, M. McDonough, JohnM . Miller et al.

Y. Siwakoti, F. Peng, F. Blaabjerg et al.

M. AlRashidi, M. El-Hawary

M. H. Nehrir, C. Wang, K. Strunz et al.

F. Blaabjerg, Huai Wang, I. Vernica et al.

The electrification of the transportation sector is moving on at a fast pace. All car manufacturers have strong programs to electrify their car fleet to fulfill the demands of society and customers by offering carbon-neutral technologies to bring goods and persons from one location to another. Power electronics technology is, in this evolution, essential and also in a rapid development technology-wise. Some of the introduced technologies are quite mature, and the systems designed must have high reliability as they can be quite complicated from an electrical perspective. Therefore, this article focuses on the reliability of the used power electronic systems applied in electric vehicles (EVs) and hybrid EVs (HEVs). It introduces the reliability requirements and challenges given for the power electronics applied in EV/HEV applications. Then, the advances in power electronic components to address the reliability challenges are introduced as they individually contribute to the overall system reliability. The reliability-oriented design methodology is also discussed, including two examples: an EV onboard charger and the drive train inverter. Finally, an outlook in terms of research opportunities in power electronics reliability related to EV/HEVs is provided. It can be concluded that many topics are already well handled in terms of reliability, but issues related to complete new technology introduction are important to keep the focus on.

Baligh Belal Yahya Mohammed Al-Hajj, Rini Nur Hasanah, Abraham Lomi

This paper shows the design and simulation of a photovoltaic (PV) power conversion system based on a Seven-Level Packed U-Cell (PUC) inverter interfaced with a Theta-Based Modulation method and a Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. The designed architecture focuses on overcoming significant challenges in PV systems, namely intermittent solar radiance, unstable voltage, and distortion of harmonics. Optimized by theta-based modulation for minimizing switching loss and overall harmonic distortion (THD), the PUC inverter topology stands out for its minimum number of components and superior output quality. A buck-boost converter controlled by the P&O MPPT algorithm is used for obtaining a maximum amount of energy from the PV array. Simulation on MATLAB/Simulink shows substantial performance improvements in terms of voltage stability, power quality, and energy efficiency under changing environmental conditions. The system integrated with the designed architecture proves a cost-effective and reliable method for improving the performance of PV systems and facilitating grid interface.

Mohammadali Ranjbar, Hamid Reza Baghaee, Amin Ramezani

The rapid proliferation of electric vehicles (EVs) has transformed power distribution networks, making the development and optimal management of Electric Vehicle Charging Stations (EVCS) a critical concern. Integrating EVCS with Renewable Energy Sources (RES) not only reduces fossil-fuel dependency but also enhances grid sustainability and lowers operational costs. However, variability in RES generation and unpredictable user charging patterns complicate station management. Strategic siting of EVCS is vital to minimizing network losses, flattening load profiles, and improving power quality. In response, this paper offers a systematic review of recent research on EVCS planning and control, examining optimization techniques for station location and capacity, advanced planning and control models, and grid-interactive charging strategies. We assess intelligent economic charging approaches—including dynamic scheduling, prediction-based control, and real-time grid interaction—and demonstrate how multi-objective optimization frameworks can reconcile cost efficiency, stability, and energy efficiency. Drawing on the IEA's Global EV Outlook 2025 projection of over 40 million EVs by 2030 (20 % CAGR since 2020), we underscore the need for scalable, RES-integrated charging frameworks aligned with techno-economic and policy-driven targets. Finally, we identify emerging trends in machine learning and artificial intelligence applications for predictive control and chart future research directions to enhance EVCS performance and grid integration.

Qidong Zhan, Xiaosong Wang, Zhiheng Zhang et al.

ABSTRACT By incorporating the advantages of magnetic gearing effect and claw pole structure, a new type of claw‐pole electric‐excitation field‐modulation (CPEEFM) machine has been developed, which has potential direct‐drive application prospects due to its flexible field regulation ability and high rotor reliability. However, the axial asymmetry and the flux distribution complexity make it difficult to analytically calculate the iron loss. The purpose of this paper is to propose an improved iron loss calculation model for this CPEEFM machine, in which the iron loss coefficients are corrected accordingly by introducing the three‐dimensional (3‐D) flux density distortion rate to fully consider the influence of axial flux distribution in addition to the radial and tangential flux distributions. Taking a 24‐slot/14‐pole CPEEFM machine as an example, its iron loss characteristics under different operation conditions are calculated by the proposed model and compared with the traditional Bertotti model and the 3‐D finite element analysis (FEA) implemented by JMAG software based on Fast Fourier Transform (FFT) model, which shows an acceptable consistency and improved accuracy. Furthermore, a prototype is finally fabricated and the experimental testing is carried out to verify the validity of the proposed iron loss analysis model.

Kangjia Zhang, Kun Zhao, Xiaoxiao Yang et al.

Traditional LLC resonant converters face significant challenges in wide-output-voltage-applications, such as limited voltage gain, efficiency degradation under wide-gain range, and increased complexity in magnetic component design. For example, in electric vehicle charging power modules, achieving wide output voltage typically relies on changing the transformer turns ratio or switching the series-parallel circuit configuration via relays, which prevents real-time dynamic adjustment. To overcome these limitations, this paper proposes a wide-gain-range control method based on a full-bridge T-type three-level LLC resonant converter, capable of achieving a voltage gain range exceeding six times. By integrating a T-type three-level bridge arm with PWM modulation and employing a variable-topology and variable-frequency control strategy, the proposed method achieves synergistic optimization for wide-output-voltage-applications. PWM modulation enables wide-range voltage output by dynamically adjusting both the converter topology and switching frequency. Finally, the proposed method is validated through circuit simulations and experimental results based on a full-bridge T-type three-level LLC converter prototype, demonstrating its effectiveness and feasibility.

Hyeongmeen Baik, Jinia Roy

This paper presents a comprehensive review of dielectric barrier discharge (DBD) power supply topologies, aiming to bridge the gap between DBD applications and power electronics design. Two key aspects are examined: the dependence of the DBD electrical model on reactor geometry, and application-driven requirements for injected waveform characteristics, including shapes, voltage amplitude, frequency, and modulation techniques. On this basis, the paper systematically reviews two major categories of power supplies: sinusoidal types comprising transformerless and transformer-based resonant inverters, and pulsed power supplies (PPSs). The review summarizes performance trade-offs, highlights untested topologies and emerging applications, and offers guidance for advancing high-performance DBD power supply design for next-generation systems.

Aisling Power, Cara-Lena Nies, Stefan Schulz

Controlling the crystal phase and lattice mismatch of semiconductors offers a powerful route to engineer electronic and optical properties of heterostructures. As a consequence, semiconductors in the wurtzite phase are increasingly sought after, superseding the thermodynamically favored cubic zinc blende phase. Empirical atomistic modeling, required for large scale simulations of heterostructures and their properties, relies heavily on valence force field (VFF) methods to find the equilibrium atomic positions in an alloy. For zinc blende crystals, VFF models are well-established. In the case of wurtzite, such parameters are frequently adopted without rigorous analysis, despite subtle but consequential differences from the zinc blende structure. Such an approach can compromise accuracy in describing material properties, since the structural differences between zinc blende and wurtzite directly influence electronic and optical characteristics. Based on the analytical VFF model by Tanner et al., and using structural similarities between wurtzite and [111]-oriented zinc blende, we construct a wurtzite VFF without introducing additional parameters. Our framework relies on analytic expressions and minimization routines to project zinc blende models onto wurtzite systems. Beyond elastic tensors, we train the model to reproduce bond length asymmetries and band gaps by using output of the VFF model in density functional theory calculations. Applied to wurtzite III-N compounds and BN, the model accurately reproduces targeted observables but also properties it has not been trained on, including the internal parameter u. We further validate the model on highly mismatched alloys such as (B,Ga)N and (B,In,Ga)N, exhibiting good agreement between VFF and density functional theory results when using identical supercells in these calculations.

Yiping Liu, Xiaozhe Wang, Geza Joos

The increasing penetration of electric vehicles (EVs) can provide substantial electricity to the grid, supporting the grids' stability. The state space model (SSM) has been proposed as an effective modeling method for power prediction and centralized control of aggregated EVs, offering low communication requirements and computational complexity. However, the SSM may overlook specific scenarios, leading to significant prediction and control inaccuracies. This paper proposes an extended state space model (eSSM) for aggregated EVs and develops associated control strategies. By accounting for the limited flexibility of fully charged and discharged EVs, the eSSM more accurately captures the state transition dynamics of EVs in various states of charge (SOC). Comprehensive simulations show that the eSSM will provide more accurate predictions of the flexibility and power trajectories of aggregated EVs, and more effectively tracks real-time power references compared to the conventional SSM method.

Muhammad Bin Fayyaz Ahsan, S. Mekhilef, T. Soon et al.

Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium‐ion battery (LIB) and a supercapacitor (SC)‐based HESS (LIB‐SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal‐sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system‐level optimizations, include component sizing (CS) and power‐energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB‐SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB‐SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB‐SC HESS are provided to inspire researchers for further studies.

Lea Dorn-Gomba, John Ramoul, J. Reimers et al.

The electric revolution is underway in the transportation sector, and the aviation industry is poised to embrace fundamental disruption. Moving to electric aircraft brings undeniable benefits in terms of environmental impact, cost savings, maintenance, noise pollution, and safety. Nevertheless, several technical challenges are yet to be overcome to build electric airplanes that meet public needs while gaining acceptance and trust. From urban air mobility to long-haul flight applications, hundreds of projects are under research to push toward more electrification. At the heart of each aircraft architecture, power electronics plays a crucial role in the new era of transportation. This article aims to provide a comprehensive analysis of state-of-the-art power electronics in electric aircraft. A review of the current status of aircraft electrification will be provided, and technology surveys of power electronic converters will be detailed. Challenges for forthcoming power electronics in response to the future trends of the electrical network will be explained. Finally, emerging technologies regarding wide bandgap devices, advanced topologies and control, thermal management, passive components, and system integration will be discussed.

HE Yuling, JIANG Mengya, QIU Minghao

In order to analyze the loss, temperature rise and the mechanical structural response under the combined magnetic tension and thermal stress of the stator core, the electromagnetic-temperature-stress coupling calculation for the stator core of a synchronous generator is carried out in this paper. Firstly, the core loss and the magnetic pull per unit area are theoretically deduced, followed by an analysis of the core's temperature rise characteristics. On this basis, the mechanical structure response of the core under the coupling excitation of magnetic pull and non-uniform thermal load is obtained. Then, a three-dimensional finite element model of the CS-5 synchronous generator is established. This model calculates the magnetic pull per unit area, loss curve and temperature distribution of the stator core. Furthermore, the deformation, strain and stress of the stator core under the simultaneous action of magnetic tension and thermal load are obtained. The results show that the stator slot temperature is highest when the generator runs stably. The deformation at the groove is largest and the stress at the bottom of the groove is higher. Finally, the temperature rises of the end face, inside slot and outside circle of the stator core are monitored in real-time by thermocouples and a temperature monitor. The measured temperature distribution of the stator core is in good agreement with the finite element simulation results, verifying the effectiveness of the electromagnetic-temperature-stress coupling method. In this paper, the temperature distribution of the stator core and the mechanical structural response distribution of the stator core under magnetic and thermal coupling excitation are obtained, providing a technical reference for the reverse optimization design of the generator structure and the prevention of stator core deformation.

Kamel BELKACEM, Noureddine BALI, Hilal LABDELAOUI

This work presents a comparative study of different meta-heuristic algorithms which are commonly used in the literature, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony and Grey Wolf Optimizer. These algorithms are applied to solve a multi-state system maintenance optimization problem considering time and system availability constraints. The objective is to find the optimal inspection and maintenance intervals for each component of the system in order to minimize the preventive maintenance cost of overall the system. The performances of the algorithms are compared using different metrics, regarding the quality and stability of the results and the speed of convergence, in order to determine the most efficient algorithms.

ZHOU Dan, YUAN Zhi, LI Ji et al.

ObjectivesThe existing hybrid energy storage system control strategy finds it difficult to maintain the state of charge (SOC) within a reasonable range while also meeting the advanced charging and discharging needs due to future wind power fluctuations. Therefore, a new advanced fuzzy control strategy for hybrid energy storage systems was proposed, which takes into account the smoothing of future wind power fluctuations.MethodsFirstly, the wind power needing to be smoothed by different types of energy storage devices was decomposed using the ensemble empirical mode decomposition (EEMD) method. Secondly, the power correction parameter was adjusted according to the SOC and power saturation level of the hybrid energy storage system to correct its output power. Thirdly, the wind power prediction algorithm was used to obtain the predicted value of wind power in the forward-looking cycle. The advance charging and discharging parameters were adjusted to correct the output power of the energy storage system based on the wind power fluctuations in the forward-looking cycle and the over-advance control theory. Finally, taking the actual data of a wind farm as an example, the validity of the proposed forward-looking fuzzy control strategy was validated through simulation.ResultsThe proposed control strategy can not only reduce the over-limit probability of wind power grid-connected fluctuation, significantly reduce the deviation between the total output power and the target power, but also maintain the SOC of the hybrid energy storage system within a reasonable range.ConclusionsThis strategy can provide a useful reference for research related to smoothing wind power fluctuations.

Rami BOUMEGOURA, Riad BENDIB, Kamel MENIGHED

This paper investigates the control of a Continuous Stirred Tank Reactor (CSTR), with a primary emphasis on achieving temperature stability and optimizing reactant conversion. The CSTR poses challenges due to its nonlinear and exothermic behavior. To address these challenges, Model Predictive Control (MPC) is employed, a powerful strategy for handling complex systems. Additionally, Particle Swarm Optimization (PSO) is introduced to fine-tune MPC parameters, ensuring optimal performance. By integrating PSO with MPC, this study enhances control capabilities, specifically targeting the intricate demands of CSTR systems.

Dominic Power, Stefan Mijin, Kevin Verhaegh et al.

Plasma-impurity reaction rates are a crucial part of modelling tokamak scrape-off layer (SOL) plasmas. To avoid calculating the full set of rates for the large number of important processes involved, a set of effective rates are typically derived which assume Maxwellian electrons. However, non-local parallel electron transport may result in non-Maxwellian electrons, particularly close to divertor targets. Here, the validity of using Maxwellian-averaged rates in this context is investigated by computing the full set of rate equations for a fixed plasma background from kinetic and fluid SOL simulations. We consider the effect of the electron distribution as well as the impact of the electron transport model on plasma profiles. Results are presented for lithium, beryllium, carbon, nitrogen, neon and argon. It is found that electron distributions with enhanced high-energy tails can result in significant modifications to the ionisation balance and radiative power loss rates from excitation, on the order of 50-75% for the latter. Fluid electron models with Spitzer-Härm or flux-limited Spitzer-Härm thermal conductivity, combined with Maxwellian electrons for rate calculations, can increase or decrease this error, depending on the impurity species and plasma conditions. Based on these results, we also discuss some approaches to experimentally observing non-local electron transport in SOL plasmas.

Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal

The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect for the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as the graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. In this survey, we focus on graph-based models for data quality control in monitoring sensor networks. Furthermore, we delve into the technical details that are commonly leveraged for providing powerful graph-based solutions for data quality tasks in sensor networks, including missing value imputation, outlier detection, or virtual sensing. To conclude, we have identified future trends and challenges such as graph-based models for digital twins or model transferability and generalization.