Superconducting technology applications in electric machines have long been pursued due to their significant advantages of higher efficiency and power density over conventional technology. However, in spite of many successful technology demonstrations, commercial adoption has been slow, presumably because the threshold for value versus cost and technology risk has not yet been crossed. One likely path for disruptive superconducting technology in commercial products could be in applications where its advantages become key enablers for systems which are not practical with conventional technology. To help systems engineers assess the viability of such future solutions, we present a technology roadmap for superconducting machines. The timeline considered was ten years to attain a Technology Readiness Level of 6+, with systems demonstrated in a relevant environment. Future projections, by definition, are based on the judgment of specialists, and can be subjective. Attempts have been made to obtain input from a broad set of organizations for an inclusive opinion. This document was generated through a series of teleconferences and in-person meetings, including meetings at the 2015 IEEE PES General meeting in Denver, CO, the 2015 ECCE in Montreal, Canada, and a final workshop in April 2016 at the University of Illinois, Urbana-Champaign that brought together a broad group of technical experts spanning the industry, government and academia.
With its remarkable electro-thermal properties such as the highest known thermal conductivity (~22 W cm−1∙K−1 at RT of any material, high hole mobility (>2000 cm2 V−1 s−1), high critical electric field (>10 MV cm−1), and large band gap (5.47 eV), diamond has overwhelming advantages over silicon and other wide bandgap semiconductors (WBGs) for ultra-high-voltage and high-temperature (HT) applications (>3 kV and >450 K, respectively). However, despite their tremendous potential, fabricated devices based on this material have not yet delivered the expected high performance. The main reason behind this is the absence of shallow donor and acceptor species. The second reason is the lack of consistent physical models and design approaches specific to diamond-based devices that could significantly accelerate their development. The third reason is that the best performances of diamond devices are expected only when the highest electric field in reverse bias can be achieved, something that has not been widely obtained yet. In this context, HT operation and unique device structures based on the two-dimensional hole gas (2DHG) formation represent two alternatives that could alleviate the issue of the incomplete ionization of dopant species. Nevertheless, ultra-HT operations and device parallelization could result in severe thermal management issues and affect the overall stability and long-term reliability. In addition, problems connected to the reproducibility and long-term stability of 2DHG-based devices still need to be resolved. This review paper aims at addressing these issues by providing the power device research community with a detailed set of physical models, device designs and challenges associated with all the aspects of the diamond power device value chain, from the definition of figures of merit, the material growth and processing conditions, to packaging solutions and targeted applications. Finally, the paper will conclude with suggestions on how to design power converters with diamond devices and will provide the roadmap of diamond device development for power electronics.
Md. Sanwar Hossain, Md. Rabiul Islam, Danny Sutanto
et al.
ABSTRACT Offshore wind and solar energy have substantial attention for the generation of high‐voltage ac (HVAC) and high‐voltage dc (HVDC). However, traditional systems face application limitations due to low‐frequency transformers and inefficient power converters. This article proposes a four‐port solid‐state transformer (FPSST) to enhance large‐scale energy generation from renewable sources. The FPSST incorporates a modular multilevel converter to collect both medium‐voltage ac and dc from wind and solar systems. Following this collection, high‐frequency transformer‐based dc/dc converters ensure galvanic isolation between ports by enabling a compact, lightweight and efficient design due to the advanced magnetic material. A cascaded H‐bridges multilevel inverter produces HVAC with reduced harmonic distortion and precise voltage regulation. Moreover, a series‐connected two‐quadrant converter generates HVDC, providing a stable dc output through a discretised dc‐link capacitor. The performance of the proposed FPSST is thoroughly investigated using the MATLAB/Simulink platform, which offers insight into system behaviour. Prior to experimental validation, an amorphous alloy‐based high‐frequency transformer is developed in the laboratory, and a 1‐kW sealed‐down FPSST is constructed. Simulation and experiment results confirm the feasibility and effectiveness of the proposed FPSST. Thanks to its compact, modular design, the four‐port SST can be easily scaled, enabling both HVAC and HVDC generation from renewable sources.
ABSTRACT High torque output capability is crucial for electric vehicle in‐wheel machines. In order to comprehensively evaluate the performance of different out rotor flux reversal permanent magnet (FRPM) machines, a comparative study of two FRPM machines with different topologies is conducted. Different from the conventional consequent pole FRPM machines (CPFRPM), the FRPM machine with auxiliary teeth (ATFRPM) features a stator tooth with a complete piece of permanent magnet (PM) of the same polarity, whereas the adjacent stator tooth is devoid of any PM. The paper introduces the topology structure of the machine using a 24‐slot/22‐pole combination and analyses its operation principle. Geometrical parameters are globally optimised to improve torque performance of FRPM machines. Furthermore, the electromagnetic characteristics of the CPFRPM and ATFRPM machines are compared under the same current density. The ATFRPM machine exhibits superior performance in efficiency, power factor and torque density. Finally, an ATFRPM machine prototype is manufactured to verify the theoretical analysis, and the experimental results confirm the effectiveness of the simulation analysis and optimised design.
Mostafa Wageh Lotfy, Haitham S. Ramadan, Sherif M. Dabour
Abstract This paper presents an enhanced Maximum Power Point Tracking (MPPT) algorithm for Quadratic-Boost Split Source Inverters (QB-SSI), designed for grid-connected Photovoltaic (PV)-powered smart charging stations for Electric Vehicles (EVs). The proposed algorithm integrates advanced control strategies and adaptive techniques to address the limitations of traditional MPPT techniques. By dynamically adjusting to operating conditions and environmental factors, the enhanced algorithm achieves accurate and rapid tracking of the maximum power point (MPP) under dynamic conditions. The paper provides a comprehensive overview of the QB-SSI topology, a detailed mathematical model, and simulation results. These findings demonstrate the superior performance of the enhanced MPPT algorithm, offering significant improvements in tracking accuracy, convergence time, and efficiency, thereby enhancing the overall performance of QB-SSI-based PV systems. A modified space vector modulation (SVPWM) is utilized to enhance the inverter switching characteristics and dc-boosting capability. The overall system is modeled via MATLAB/Simulink™, and the experimental results providing valuable insights into the performance and functionality of the proposed algorithm. This allows for a comprehensive analysis of its capabilities and potential advantages in practical applications.
Van Nhat Nguyen, An-Giang Nguyen, Thi Viet Bac Phung
et al.
Abstract Carbon-based supercapacitor electrodes derived from biomass have recently garnered significant attention due to their low cost, natural abundance, and environmental sustainability. In this study, charcoal was pretreated using a simple ultrasonic method and was employed as the active electrode material in both three-electrode and symmetric supercapacitor configurations. To further enhance electrochemical performance, a sustainable and dual-functional strategy was implemented by introducing methylene blue, a redox-active additive, into an aqueous sodium chloride electrolyte. Structural and morphological characterizations revealed that charcoal possessed a highly porous architecture with preserved plant-based vascular channels, facilitating efficient electrolyte penetration and ion transport. Electrochemical analyses demonstrated that the incorporation of methylene blue significantly enhanced charge storage through a synergistic combination of electric double-layer capacitance and pseudocapacitive behavior. The optimal device, utilizing the MB35 electrolyte composition, delivered a high specific capacitance of 212.23 F g–1 at 0.5 A g–1, an energy density of 15.34 Wh kg–1 at a power density of 350 W kg–1, and excellent cycling stability, retaining 91.3% of its initial capacitance after 2000 cycles and 84.3% after 5000 cycles at the high loading mass of 2 mg cm− 2. This work presents a cost-effective route for fabricating high-performance biomass-derived supercapacitors while offering a novel approach for the reutilization of dye pollutants in sustainable energy storage applications.
The mass and volume of wireless power transfer (WPT) systems for charging electric vehicles are directly related to the rated power of the system. The difficulties of high-power wireless charging are exacerbated by the need to meet the same practical constraints associated with vehicle integration as lower power systems. Therefore, more advanced techniques are necessary to improve power density and specific power of wireless charging systems for high-power applications. This article presents theory and analysis of three-phase inductive WPT systems with bipolar phase windings. Magnetic coupler topologies and the theoretical and practical aspects of series three-phase resonant compensation networks are discussed. The systems under consideration are designed to utilize rotating magnetic fields to achieve a power transfer characteristic that is temporally smoother than single-phase systems. Other benefits associated with rotating magnetic field based WPT, including reduced ferrite mass, filter component requirements, and electromagnetic field emissions, are discussed. Experimental results of a prototype system are presented in both aligned and misaligned configurations. The system is demonstrated transferring 50 kW with 95% dc-to-dc efficiency over a 150-mm airgap in the aligned case. On a per-pad basis, the magnetic couplers achieve a power density of 195 kW/m$^2$ and a specific power of 3.65 kW/kg. This article is accompanied by a video of the rotating magnetic field produced by a simulated three-phase WPT system.
Capacitive power transfer (CPT) technology is an effective and important alternative to the conventional inductive power transfer (IPT). It utilizes high-frequency electric fields to transfer electric power, which has three distinguishing advantages: negligible eddy-current loss, relatively low cost and weight, and excellent misalignment performance. In recent years, the power level and efficiency of CPT systems has been significantly improved and has reached the power level suitable for electric vehicle charging applications. This paper reviews the latest developments in CPT technology, focusing on two key technologies: the compensation circuit topology and the capacitive coupler structure. The comparison with the IPT system and some critical issues in practical applications are also discussed. Based on these analyses, the future research direction can be developed and the applications of the CPT technology can be promoted.
Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schemes are limited by the increasing variations and uncertainties occur in power systems, where the adjustment problems of different transmission interfaces are often treated as several independent tasks, ignoring their coupling relationship and even leading to conflict decisions. In this paper, we introduce a novel data-driven deep reinforcement learning (DRL) approach, to handle multiple power flow adjustment tasks jointly instead of learning each task from scratch. At the heart of the proposed method is a multi-task attribution map (MAM), which enables the DRL agent to explicitly attribute each transmission interface task to different power system nodes with task-adaptive attention weights. Based on this MAM, the agent can further provide effective strategies to solve the multi-task adjustment problem with a near-optimal operation cost. Simulation results on the IEEE 118-bus system, a realistic 300-bus system in China, and a very large European system with 9241 buses demonstrate that the proposed method significantly improves the performance compared with several baseline methods, and exhibits high interpretability with the learnable MAM.
ObjectivesThe preliminary research device of magnetic confinement deuterium-deuterium fusion neutron source is a novel neutron source preliminary research device based on field-reversed configuration (FRC) cascade magnetic compression. It aims to leverage the experiences from the first-phase construction to enhance system design, significantly improve plasma parameters, and further expand research on magnetic compression fusion, laying the foundation for achieving a large-volume high-flux fusion neutron source in the third phase.MethodsThe preliminary research device control system optimized and reconstructed the control framework, provided safety interlocking, pulse control and comprehensive data services, coordinated and integrated each service into the automated discharge process through integrated control, and added a number of resources to expand applications and DevOps tool.ResultsThrough the reconfiguration design, the comprehensive performance of the control system in terms of safety, stability and efficiency had been significantly improved. The safety interlock system ensured the safety of personnel and equipment during the experiment process, the pulse control system achieved high-precision timing control, the comprehensive data service provided full process support from data collection to analysis, and resource expansion applications and DevOps tools further improved the system flexibility and operation and maintenance efficiency.ConclusionsBy optimizing the control framework and introducing advanced operation and maintenance tools, the design can better meet the needs of complex device structure and precise discharge flow, and provide an efficient control system construction plan for the subsequent long-term cooperation construction of the magnetically confined deuterium fusion neutron source preliminary research device.
Applications of electric power, Production of electric energy or power. Powerplants. Central stations
In this work, a high-k In0.53Ga0.47As silicon-on-insulator FinFET (InGaAs–SOI–FinFET) is presented for high-switching and ultra-low power applications at 7 nm gate length. Indium Gallium Arsenide (InGaAs) is a compound semiconductor that has gained attention in the field of semiconductor devices, including FinFETs. The incorporation of InGaAs in proposed FinFETs introduces several advantages, making it an attractive material for certain applications. InGaAs–SOI–FinFET performance has been observed and found high electron mobility, improved On-Current performance (ION), drain current (IDS), transconductance (gm), energy bands, lower subthreshold swing (SS), electric field, surface potential, and better short-channel behaviour. All the results of InGaAs–SOI–FinFET have been simultaneously compared with SOI-FinFET and conventional FinFET (C-FinFET). Incorporating InGaAs in the channel with high-k gate material enhances the drain current by ⁓75% and ⁓77% in the proposed device compared to the other two counterparts. Owing to the higher drain current in the InGaAs–SOI–FinFET, other parameters have also been improved, which leads to higher performance applications.
Electric apparatus and materials. Electric circuits. Electric networks, Computer engineering. Computer hardware
The multi-uncertainty of source and load poses significant challenges to the optimal scheduling of 'source-load-storage' integrated microgrid. However, a limitation of the traditional optimization model is its one-sidedness and use of a single time scale, which can result in suboptimal scheduling outcomes. Striking a balance between reliability and economy presents a considerable obstacle, as does coordinating the relationship between uncertainty analysis methods and varying time scales. Based on the data-driven multi-discrete scene distribution robust method, a two-stage distributed robust day-ahead optimal scheduling model of microgrid is proposed, which is solved by column and constraint generation algorithm. By combining the improved distributed robust optimization uncertainty method with a multi-time scale scheduling strategy and model predictive control theory, the accuracy of the scheduling can be enhanced. This is achieved through the gradual refinement of the scheduling time scale and reduction of the prediction period length. The day-ahead-day multi-time scale rolling optimization scheduling model is established to minimize the generation cost and adjustment cost, while also exhibiting a high degree of resilience to system uncertainties. Combined with the simulation analysis of the example, the proposed model has demonstrated advantages in incorporating new energy sources, reducing operating costs, and balancing considerations of safety and economy.
Abstract The Permanent Magnet Linear Motor (TPMLM) is widely used in different industrial fields. TPMLMs with slots and iron cores have high power density, but their thrust fluctuations and copper losses are significant. Due to the nonlinearity and saturation of magnetic circuits, their electromagnetic models are complex and the accuracy of numerical methods is very inferior. Substantially accurate modelling is crucial for motor optimisation design. In this paper, a data‐driven modelling method based on Bayesian optimisation deep neural network (DNN) is proposed to improve the accuracy of the electromagnetic field. The finite element (FE) modelling under different structural parameters is analysed and provides a training dataset for DNN. Then, a multi‐objective optimisation problem for the slotted TPMLM is carried out based on the multi‐objective black hole algorithm. Compared to the original design, the average thrust of TPMLM increased by 49.37%, the thrust fluctuation percentage decreased by 9.59%, and the coil copper consumption percentage decreased by 2.64%. The results show that the improved DNN model has very high modelling accuracy, providing a new way for motor design and optimisation.
In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.
Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems in milliseconds. Indeed, PDL-SCOPF remedies the limitations of supervised counterparts that rely on training instances with their optimal solutions, which becomes impractical for large-scale SCOPF problems. PDL-SCOPF mimics an Augmented Lagrangian Method (ALM) for training primal and dual networks that learn the primal solutions and the Lagrangian multipliers, respectively, to the unconstrained optimizations. In addition, PDL-SCOPF incorporates a repair layer to ensure the feasibility of the power balance in the nominal case, and a binary search layer to compute, using the Automatic Primary Response (APR), the generator dispatches in the contingencies. The resulting differentiable program can then be trained end-to-end using the objective function of the SCOPF and the power balance constraints of the contingencies. Experimental results demonstrate that the PDL-SCOPF delivers accurate feasible solutions with minimal optimality gaps. The framework underlying PDL-SCOPF aims at bridging the gap between traditional optimization methods and machine learning, highlighting the potential of self-supervised end-to-end primal-dual learning for large-scale optimization tasks.