Search Results (26,240)

The adjustable speed hydro-generating unit has a complex three-phase alternating current excitation structure. The existing rotor winding short circuit (RWSC) fault diagnosis methods are generally difficult to use to locate the fault location and identify the severity of the fault. Therefore, an offline diagnosis method for the internal RWSC of an adjustable speed hydro-generating unit is proposed in this paper. Firstly, after the unit is shut down, the low-voltage pulse signal is repeatedly injected into the rotor winding by the pulse generator. By comparing and analyzing the voltage response characteristics under different types of short circuit faults, an identification method of rotor winding short circuit fault type and fault phase based on detecting the reverse polarity sub-spike is proposed. Furthermore, the short circuit fault point can be accurately located by combining ensemble empirical mode decomposition (EEMD) with the Teager energy operator (TEO). Finally, the fault factor is constructed based on the area between the characteristic waveform and the zero line, and the quantitative evaluation of the severity of the short circuit fault is realized based on this. The effectiveness of the proposed fault diagnosis and location method is verified by the simulation results. Full article

With the continuous integration of a large amount of renewable energy sources such as wind and solar power into the power system, the economic and secure scheduling of the power grid, as a crucial carrier for electricity transmission, becomes of paramount importance. However, issues such as voltage fluctuations at grid nodes, low renewable energy consumption rates, and increased active power losses, caused by the widespread integration of high proportions of renewable energy, urgently need to be addressed. To effectively solve these problems, this paper proposes a multi-objective coordinated optimization scheduling method for the economy and security of source–grid–load–storage based on an effective scenario-screening approach. Firstly, an iterative self-organizing data analysis algorithm based on density noise application spatial clustering is designed to efficiently generate typical output scenarios for renewable energy sources such as wind and solar power. Meanwhile, to achieve low-carbon scheduling objectives, green certificate and carbon trading mechanisms are introduced. A multi-objective coordinated scheduling and trading model for the economy and security of large power grids, sources, loads, and storage is constructed with the goal of enhancing renewable energy consumption, and it is solved using the weight assignment method and an improved particle swarm optimization algorithm. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated based on an improved IEEE standard node test system. Full article

Although electric vehicles are being vigorously promoted around the world, the mileage anxiety problem is an important hindrance to their development. Thus, this paper proposes an auxiliary half-bridge dual active bridge (AH-DAB) converter between different electric vehicles, which is based on nonlinear virtual power predictive control. For the converter, characteristics of high power density, wide voltage gain range, and high efficiency are necessary. Firstly, an AH-DAB converter is applied to improve the control variable. Under this effect, the converter can switch between the half-bridge and the full-bridge converter. Secondly, a duty ratio design method is proposed to improve zero-voltage switching (ZVS) performance. Therefore, wide voltage gain range, decoupling of control variables, and high efficiency can be achieved in the nonlinear AH-DAB system. Thirdly, the nonlinear virtual power predictive control is proposed to ensure energy transfer between two electric vehicles. Based on this, the phase shift angle can be predicted and adjusted by ensuring that the actual power is consistently maintained close to the reference power. Moreover, the virtual power is generated to represent the reference power, which can reduce the number of current sensors. Finally, simulation and experiment results collectively show the wide voltage gain range and high efficiency of the proposed AH-DAB converter. Full article

To address the issues of load aggregation and traffic congestion in faulty electricity–transportation coupled networks (ETCNs), this paper proposes an electric vehicle (EV) charging guidance strategy based on Graph Reinforcement Learning (GRL). First, a graph-structured feature extraction model is developed. The GraphSAGE module is employed to capture the multi-scale spatiotemporal features of the ETCN. The topological changes and energy-information interaction characteristics under fault scenarios are analyzed. Second, a Finite Markov Decision Process (FMDP) framework is established to address the stochastic and dynamic nature of EV charging behavior. The charging station selection and route planning problem is transformed into an agent decision-making process. A reward function is designed by incorporating voltage constraints, traffic flow constraints, and state-of-charge margin penalties. This ensures a balanced consideration of power grid security and traffic efficiency. The FMDP model is then solved using a Deep Q-Network (DQN) to achieve optimal EV charging guidance under fault conditions. Finally, case studies are conducted on a coupled simulation scenario consisting of an IEEE 33-node power distribution system and a 23-node transportation network. Results show that the proposed method reduces the system operation cost to 218,000 CNY, controls the voltage deviation rate of the distribution network at 3.1% in line with the operation standard, and enables the model to achieve stable convergence after only 250 training episodes. It can effectively optimize the charging load distribution and maintain the voltage stability of the power grid under fault conditions. Full article

Wireless Power Transfer (WPT) for electric vehicles is transitioning from laboratory prototypes to deployable charging infrastructure, driven by the demand for safer, automated, and weather-robust charging in residential parking, depots, and public bays, and more recently by pilot electric-road concepts. This review focuses on near-field resonant inductive WPT and explicitly frames the discussion around standardization and deployment readiness, with SAE J2954 and related international frameworks as reference points for interoperability, alignment, conformance testing, and certification planning across static, quasi-dynamic, and dynamic solutions. Recent surveys and representative demonstrators are synthesized to consolidate dominant research and engineering themes, including magnetic coupler and shielding design, compensation-network and control co-design, segment architecture and handover strategies for dynamic tracks, safety functions, electromagnetic exposure verification, electromagnetic compatibility constraints, bidirectional operation, and data-driven methods supporting design and field adaptation. For light-duty static charging, interoperable pad families, alignment procedures, and mature compensation topologies enable repeatable high-efficiency operation and increasingly standardized validation workflows, supporting early commercial availability. Heavy-duty depot charging appears technically attractive where duty cycles favor opportunity charging and packaging constraints are manageable. Dynamic WPT has reached pilot readiness via segmented selective-energization tracks and coordinated localization and handover, but corridor-scale rollout remains limited by maintainability, seasonal reliability, cost per kilometer, and route and site-specific verification of safety, exposure, and EMC margins. Full article

Driven by the increasing demand for high power density in modern power electronic converters, this paper proposes two novel packaging designs based on the concept of an overlaying chip placement structure, including the design with a two-layer substrate (designated as M2) and the one with a three-layer substrate overlaying structure (designated as M3). Electrical and thermal simulations demonstrate that M2 achieves a 32.78% volume reduction while incurring a 12.70% increase in average thermal resistance, and a 5.72% reduction in power loop parasitic inductance compared to the conventional packaging design (designated as M1), representing a balance between compact packaging and electrothermal performance. Meanwhile, M3 achieves an ultra-low loop inductance of 2.02 nH thanks to the mutual inductance cancellation effect; however, the physical volume is increased by 38.17%, and the thermal resistance is reduced by 1.59% compared to the M1 design. The prototype of the M1 power module has been fabricated for experimental validation. Double-pulse testing and steady-state thermal resistance measurements are conducted based on the M1 prototype to confirm the accuracy of the simulation model. Full article

Active suspensions in automotive applications are designed to improve vehicle stability and comfort and reduce vibration transmission from the road surface. Active systems often include a dedicated actuator, and, to reduce their mass and energy absorption, it is a typical choice to rely on brushless electric motors with permanent magnets containing Critical Raw Materials such as Neodymium, a Rare Earth Element (REE), offering favorable power density values. Although these systems offer clear advantages in terms of ride quality and performance, their direct and indirect energy requirements, combined with their dependence on resource-intensive materials, raise concerns about life cycle sustainability: in other words, there is a trade-off between production impact (relevant for REE) and use impact (reduced by REE adoption). To address this issue, the research proposes a method to estimate energy consumption during the use phase of a vehicle through a dedicated parametric modeling and simulation framework; the aim is to evaluate the energy performance of active suspension systems under different road and driving conditions. The analysis explores how design parameters and operational choices affect energy consumption and efficiency. The simulation results reveal a marked sensitivity of system performance to road profiles and driving scenarios, highlighting the importance of holistic assessments during the early stages of design. The proposed framework represents a first step toward integrating circular design principles into the development of active suspensions. By combining technical and environmental perspectives, it supports the development of next-generation automotive components that balance comfort, performance, and sustainability. Full article

We present a study to assess the feasibility and implications of replacing internal combustion engine vehicles (ICEVs) with battery-powered electric vehicles (EVs) in a car-sharing fleet. For the analysis, we used operational data from a local car-sharing company, which encompasses various aspects such as trip distance, start and duration, vehicle type, and pickup and return locations. To evaluate the impact of transitioning the entire fleet to EVs, we used EV and charger models to simulate the battery-powered trips and the necessary post-trip recharging. Both could affect the service quality of car-sharing services, as the requested trip distance might not be covered by an electric vehicle due to range or charging time limitations. Specifically, in our simulation-based analysis, we identified chains of consecutive bookings as a critical factor for car-sharing electrification. Furthermore, to assess the potential impact of electrification on the energy grid, we used data about the local grid load and its composition to relate it to the predicted vehicle charging times. This is an extended version of our previous paper, incorporating an additional dataset. Full article

Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in high-performance electric vehicles due to their power density; however, conventional field-oriented control (FOC) relies on simplified models in which electromagnetic torque is described as a function of the quadrature current component, together with constant parameters and idealized trajectories in the $i_d-i_q$ plane, limiting adaptability and reducing efficiency and operating range under real conditions. This work introduces a flux linkage vector decomposition approach for SPMSMs, in which the permanent-magnet flux is decomposed into d- and q-axis components under core saturation and integrated into an extended field-oriented control framework. An extended FOC strategy is proposed that incorporates flux linkage vector decomposition, nonlinear magnetic saturation, cross-coupling effects, and nonlinear dependencies of electrical parameters, along with resolver angle correction and dynamic modulation index management. These enhancements modify torque and voltage trajectories by shifting the voltage-limit center and improving the definition of the MTPA, FW, and MTPV regions to better match real motor behavior, enabling performance improvements. Experimental validation on an automotive powertrain using a vehicle control unit (VCU) and precalculated lookup tables (LUTs) demonstrates improvements of up to 13.5% in low-speed torque, 13.7% in high-speed power, and efficiency gains of 4–8% across operating conditions. Full article

Two-dimensional materials have broad application prospects in the field of optoelectronic devices. As a next-generation power electronic device, AlN materials have obvious advantages in power processing, and their monolayer structure has excellent optoelectronic properties, which is of great significance for the study of 2D AlN monolayers. Properties such as electronic and optical properties of metal-adsorbed AlN (M-AlN) systems have been systematically investigated using density functional theory from first principles. The results of the energy bands of the M-AlN system indicate that the adsorption of Al, Li, Ag, Au, Bi, Cr, Mn, Na, Pb, Sn, Ti, and K metals makes the monolayer AlN magnetic, the incorporation of two metals, Al and Li, is the transition of the monolayer AlN from a semiconductor to a semi-metal, and the introduction of K metal makes the monolayer AlN transition from a semiconductor to a metal. The work function of the M-AlN system shows that the introduction of the metal reduces the work function of the monolayer AlN, especially for K-AlN, which is reduced by 56.12% compared to the monolayer AlN. In addition, the results of the optical absorption spectra of the M-AlN system revealed that the introduction of the metals made the monolayer AlN exhibit high absorption peaks in the visible and near-infrared regions; in particular, the intensity of the absorption peaks of the Ti-AlN system at 557.8 nm reached 7.4 × 10⁴ cm⁻¹ and the intensity of the absorption peaks of the K-AlN system at 1109.3 nm reached 1.01 × 10⁵ cm⁻¹. This indicates that the introduction of Ti and K metal atoms enhances the absorption properties of monolayer AlN in the visible and near-infrared regions. Finally, the time-domain finite difference using spherical metal nanoparticles is used to excite the localized surface plasmon resonance, and the results show a small area of strong electric field around the electric field hotspot of Cr and Li particles, and a good concentration of the electric field strength in the x and y directions. In summary, the system of metal atoms adsorbed on AlN will be favorable for the design of spintronics, field-emitting devices and solar photovoltaic devices. Full article

This paper investigates post-disaster restoration and stable operation of three-phase unbalanced distribution networks with high penetration of distributed generation and electric-vehicle integration. A two-stage hierarchical restoration and coordinated dispatch strategy is developed to improve restoration feasibility and reliability while reducing post-fault restoration cost. In Stage I, island partitioning and load prioritization are carried out using DFS and BFS to maximize the restoration of critical loads. In Stage II, without reducing the Stage I restoration level, branch switch statuses and coordinated outputs of DG-EV-BESS are jointly optimized under three-phase unbalanced power flow, radiality, and other operational constraints. The objective minimizes the total restoration cost, including network losses, switching operations, three-phase voltage-unbalance penalties, and customer compensation. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method reduces restoration cost and improves the system restoration ratio. In addition, phase-wise EV dispatch provides targeted compensation for weak-phase power deficits and improves post-fault inter-phase operating quality. The results validate the effectiveness of the proposed strategy. Full article

Ultrasonic motors have attracted considerable attention in precision actuation applications because of their advantages over conventional electromagnetic motors, such as compact structure, high positioning accuracy, immunity to electromagnetic interference, noise-free operation, and suitability for low-temperature environments. However, conventional traveling-wave linear ultrasonic motors usually rely on boundary constraints to establish stable traveling waves, which may limit their structural flexibility and self-propelled capability. To address this issue, this paper proposes a free-boundary traveling-wave linear ultrasonic motor capable of realizing self-propelled motion. The motor features a projection structure at each end of the stator. Two piezoelectric ceramics are placed at one end for excitation, while a damping material is arranged at the other end for energy absorption. This design enables the motor to generate traveling waves without requiring fixed boundary conditions. The motor operates in the B(3,1) out-of-plane vibration mode to enhance the energy absorption capacity of the non-excited end and reduce its standing wave ratio (SWR). A finite element model of the motor is established to investigate its vibration characteristics. In addition, a novel method for estimating the standing wave ratio is proposed by using piezoelectric ceramics attached to the motor surface, replacing the traditional calculation approach. A prototype is fabricated to verify the feasibility of the proposed design. Experimental results show that the prototype achieves a minimum SWR of 1.81, a no-load speed of 42.1 mm/s, and a maximum output force of 0.465 N. These results confirm the feasibility of the proposed scheme and provide a new approach for the design of free-boundary traveling-wave linear ultrasonic motors. Full article

The accelerating integration of electric vehicles (EVs) presents considerable operational challenges for distribution networks, particularly through aggravated voltage deviations and compromised protection coordination during periods of simultaneous charging. In response, this study introduces a novel protection-constrained Binary Evolutionary Algorithm (BEA) designed for expedited electric vehicle-oriented Distribution Network Reconfiguration (DNR) to enhance EV hosting capacity without necessitating costly infrastructure upgrades. The proposed framework uniquely embeds the inverse time–current characteristics of protective fuses—termed Protection Curve Consideration (PCC)—within the optimization process. By explicitly accounting for the thermal inertia of protection devices, the algorithm identifies reconfiguration strategies that uphold voltage stability under elevated EV transportation loading, including configurations typically deemed infeasible by conventional voltage-driven approaches. This selective coordination precludes unnecessary fuse operations, thereby preserving the continuity of electric vehicle charging services. Simulation results on a 16-bus radial distribution system, evaluated under four high-demand scenarios reflective of concentrated EV transportation charging, validate the efficacy of the BEA-PCC methodology. The approach achieves a maximum voltage deviation reduction of up to 15.2%, thereby enhancing power quality for all consumers. Moreover, compared to standard metaheuristic techniques, it reduces Energy Not Supplied (ENS) by 8% and switching operations by 20%, contributing to improved grid resilience and operational efficiency. These outcomes underscore the potential of BEA-PCC as an effective real-time control strategy for distribution system operators seeking to accommodate increasing electric vehicle penetration while safeguarding protection coordination and minimizing customer disruptions. Full article

The unit costs of power generation of onshore wind and photovoltaics in China have dropped rapidly and significantly since 2010. Recent studies have indicated that the learning effect on cost reduction could have been overestimated due to the exclusion of the equipment-level installed capacity and the price of capital. To address this estimation bias, we constructed a research framework comprising a one-factor analysis model (OFAM), a two-factor analysis model (OFAM), and a multi-factor analysis model (MFAM) based on the Cobb–Douglas function and the cost minimization problem. This framework examines the determinants of unit costs in renewable energy generation in consideration of learning effects, scale effects, and price effects. This paper uses data from institutions such as IRENA and the World Bank to empirically analyze the contributions of these factors to reductions in the cost of onshore wind and photovoltaic power generation in China from 2010 to 2022. The results indicate that the learning-by-doing (LBD) effect has been overestimated, with scale effects accounting for a significant portion of the cost reduction. Moreover, the price of capital exerts a more pronounced influence on the levelized cost of electricity (LCOE) for photovoltaics. After factoring in equipment scale and capital costs, LBD continues to significantly reduce the LCOE of photovoltaics, with the LBD learning rate declining from 23.85% to 6.30%. Meanwhile, the impact of LBD on the LCOE of onshore wind technology ceases to be significant. Both technologies exhibit economies of scale, with scale effects accounting for 41.60% and 34.12% of the LCOE reductions for onshore wind and photovoltaics, respectively. Capital costs accounted for 32.50% of the LCOE reduction for photovoltaics. Therefore, future large-scale deployments of other costly renewable energy technologies may also benefit from the equipment-level scale and favorable bank interest rates in addition to learning-by-doing. Full article