Search Results (3,673)

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

Voltage sags pose severe risks to sensitive equipment in modern industries, requiring power quality monitoring equipment to possess fast and accurate sag detection capabilities. The traditional second-order generalized integrator (SOGI) will have oscillation phenomena in the case of DC offset, low-frequency harmonics, and high-frequency impulse noise. This study introduces a strong detection algorithm that combines Adaptive Composite Morphological Filtering (ACMF) with an improved Mixed Second- and Third-Order Generalized Integrator (MSTOGI). First, the ACMF pre-filtering module dynamically adjusts the scale of composite structuring elements through periodic parameter optimization, effectively filtering high-frequency random impulses while preserving the sharp transitions of abrupt voltage changes. Second, MSTOGI eliminates DC offset, and optimizes the gain coefficient to achieve the best dynamic response speed. Ultimately, a cascaded notch filter (CNF) module focuses on and removes even-order harmonic ripples caused by the synchronous reference frame transformation. Simulation results indicate that under severe grid conditions involving multiple composite distortions, the proposed architecture reduces the sag detection time to within 1.0 ms under typical operating conditions, with steady-state phase errors strictly controlled within a ±2° range. This method provides a reliable solution for DVR and UPS. Full article

As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, $X_d$, $X_q$, $X_d^{\prime }$, $X_q^{\prime }$, $T_{do}^{\prime }$, $T_{qo}^{\prime }$) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment. Full article

This paper presents an integrated mathematical model to improve the safety and operational efficiency of train traffic in centralised railway dispatching systems. The proposed approach combines the alternative graph model with a Mealy automaton to synchronously address route planning, delay minimisation, and strict compliance with safety requirements. Formal control theory based on finite-state automata is employed to describe routing logic and signal control through state transitions, while the alternative graph model represents scheduling constraints and resource conflicts. To enhance real-time adaptability, a tabu search algorithm is implemented for train schedule optimisation, enabling dynamic rescheduling under changing operational conditions. The mathematical formulation incorporates blocking time parameters, a system of discrete constraints, and automaton-based safety conditions governing train movements and route authorisation. The integrated model explicitly formalises the processes of block section occupation and release, ensuring consistency between control logic and scheduling decisions. Practical testing and computational experiments demonstrate that the proposed approach effectively reduces train delays, improves the reliability of dispatch control, and increases system resilience to dynamic disturbances. The results confirm that the developed model can be implemented within existing centralised dispatching infrastructures without requiring a complete system overhaul. Overall, the proposed framework expands the functional capabilities of centralised dispatch systems by enabling efficient schedule generation, minimising the propagation of delays, and ensuring reliable command exchange between central control posts and field-level railway infrastructure. Full article

Advanced mobile communication standards of the fifth and subsequent generations widely use beamforming technology. While many publications on this topic rely on simulation tools, some work has been dedicated to experimental testing using software-defined radio (SDR) platforms. These platforms are often expensive and require significant expertise to configure. This paper proposes a novel cost-effective method for combining a pair of dual-channel Universal Software Radio Peripheral (USRP) B210 boards into a four-element antenna array direction of arrival estimation testbed using Metronom synchronization devices. The hardware and developed software implementation is detailed, including the antenna layout and software modules, based on USRP Hardware Driver, that provide the frequency and time synchronization necessary for amplitude-phase processing. Experimental validation of the testbed using the MUltiple SIgnal Classification (MUSIC) algorithm demonstrates high stability of angle of arrival estimates, with a standard deviation not exceeding 0.4°. The algorithm achieved a resolution of 16.1° for two sources, which surpasses the half-power beamwidth of 25.6°. The theoretical significance of this work lies in the scientific validation of combining SDR devices with the precise synchronization required for beamforming. Its practical value is in enabling the experimental testing of beamforming without the need for costly multichannel SDR hardware. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article

Rotor-flexible casing rubbing can induce strong nonlinear dynamics in rotor systems. This study investigates the harmonic frequency characteristics of a rubbing rotor system with a flexible casing constraint. A nonlinear rub-impact model combined with a finite element-based rotor–casing coupling framework is developed to evaluate system responses under concentric and eccentric configurations. The harmonic components of rotor and casing vibrations are analyzed over a range of rotational speeds. Results show that, under concentric conditions, harmonic frequencies originate from rubbing-induced asynchronous motion. The harmonic sub-frequencies observed in the spectrum correspond to lobed rotor orbits formed during the transition from synchronous to asynchronous motion under continuous rubbing forces. Under eccentric rotor–casing alignment, the vibration spectrum becomes more complex and exhibits frequency clustering. The results provide insight into harmonic generation mechanisms and highlight the role of casing flexibility in rubbing-induced asynchronous motion. Full article

Virtual Synchronous Generator (VSG) has become a prominent candidate to control grid-tied power electronic inverters for its ability to provide inertial support and improve power system frequency stability. However, under disturbances, VSG exhibits significant oscillations in its output frequency and power. Meanwhile, existing oscillation suppression methods rely on somewhat complex modeling and cumbersome parameter tuning. To address this issue, this paper proposes a straightforward approach to improving the transient performance of VSG based on the equivalent circuit model of the VSG active power loop. First, it is shown that the parameters in the VSG active power loop have a one-to-one correspondence with the elements of a RLC circuit. Based on the equivalent circuit model of VSG control, it is demonstrated that under the constraints of ROCOF and power–frequency droop limitation, oscillation suppression cannot be effectively achieved only by parameter tuning. Thus, an additional damping resistance branch is introduced into the VSG equivalent circuit model. The quantitative parameter design method of this damping branch is further introduced. Finally, high-power experiments demonstrate that the proposed method effectively suppresses power oscillations and enhances the transient performance of VSGs. Full article

Interest in isolated electrical systems powered by renewable energy has driven the development of alternatives to traditional Wind–Diesel Systems (WDS) due to their unwanted emissions and regulatory constraints. In this context, clean and efficient hybrid architectures are needed to comply with regulations and ensure stable operation under variations in user load and wind generation. This paper proposes an integrated isolated hybrid system consisting of a fuel cell replacing the Diesel Generator (DG). To fulfil the role of the synchronous generator in the diesel-group, the fuel cell operates under a Grid-Forming (GFM) control scheme, acting as a virtual synchronous machine that establishes the system’s voltage and frequency. The main aim of the hybrid system is for the wind turbine to supply most of the active power to the loads, thereby minimising hydrogen consumption. A key challenge in these systems is maintaining power balance, particularly preventing reverse flows in the fuel cell system, which has less margin than the diesel generator. In this paper, a Dump Load (DL) quickly dissipates excess power and prevents reverse power conditions. Overall, the proposed system eliminates the need for diesel generation, thereby eliminating emissions while maintaining operational stability. Simulation results demonstrate the correct functioning of the system in the presence of significant variations in load and wind power generation. Full article

Accurate and fast junction-temperature estimation in Silicon Carbide (SiC) power modules is crucial for reliable operation, health monitoring and predictive control of power electronic converters in different applications. However, direct temperature measurement inside the module is difficult and high-fidelity thermal models are often very computationally expensive for real-time implementation. This paper proposes a digital twin development approach for fast and accurate temperature estimation in all three dimensions of a SiC MOSFET power module by a combination of finite element method (FEM) modelling and neural networks. The work is especially relevant in thermal monitoring and managing power electronics converters such as renewable energy systems, energy storage systems, Electric Vehicles (EV), etc. The model incorporates a neural network trained on data generated from an FEM model built in COMSOL Multiphysics. The developed digital twin can estimate the temperature distribution, including the ten junction temperatures of the Wolfspeed EAB450M12XM3 module, with an average estimation time of 0.063 s, enabling predictive control. In order to improve practical applicability and model synchronization with the physical system, NTC-based feedback techniques are discussed (single-Temperature Coefficient (NTC) and double-NTC approaches). The proposed framework is investigated in terms of prediction accuracy and computational performance related to the FEM-generated reference data. The approach improves model reliability by adjusting the parameters of the critical digital and physical modules. The combination of FEM-based modelling and machine learning can provide a foundation for accurate, real-time thermal monitoring in power electronic modules. Full article

Tidal energy is increasingly harnessed due to its high energy density, substantial reserves, and reliable predictability. However, marine fouling on turbine blades adds weight and induces asymmetric system loads; prolonged operation exacerbates generator magnetic saturation, causing inductance parameter deviations from controller presets, which further leads to current loop delays, amplified tracking errors and unstable power output. To mitigate these issues, a self-tuning inductance-oriented model-free predictive current control method is proposed. The proposed method utilizes a simplified hyperlocal model alongside an extended state observer to effectively counteract the effects of non-inductive parameters. Simultaneously, the incremental model coupled with a dynamic adjustment method is proposed for real-time adaptive inductance tuning. Simulation results demonstrate that the proposed method significantly enhances system robustness against inductance mismatches and reduces parameter sensitivity, thereby ensuring stable operation. Compared with traditional PI control and model predictive control strategies, the proposed approach exhibits superior performance in disturbance rejection, parameter adaptability, and operational stability. Full article

The leg structure of swine is closely related to their robustness and longevity. Animals with sound legs generally have longer productive lifespans and higher reproductive efficiency, whereas leg defects can markedly impair performance and shorten service life. To address the high subjectivity, low efficiency, and poor consistency of traditional leg-structure evaluation by humans, this study developed an automatic scoring system for swine leg structure based on 3D point clouds. The hardware components of the system include the acquisition channel, a multi-view time-of-flight (ToF) depth camera array, an industrial computer, and a star-type synchronization hub. The core algorithm modules include point cloud preprocessing, leg segmentation, geometric feature extraction, and structure-based scoring. Body orientation was corrected using principal component analysis (PCA). An adaptive limb region segmentation method was proposed that combines iterative cropping with geometric verification. Two point cloud tasks were performed: key structural points were extracted via multi-scale curvature analysis, and angular and symmetry parameters of the fore- and hindlimbs were computed in the sagittal and coronal planes. Following a “classify first, then score” strategy, a nine-level linear scoring model was constructed. Field validation showed that the classification accuracy exceeded 90%, the scores were significantly negatively correlated with the degree of structural deviation, and multi-frame resampling yielded good repeatability. The processing time per animal ranged from 1.6 s to 3.0 s, which met the requirements for real-time applications. These results demonstrated that the proposed method could automatically identify and quantitatively evaluate swine leg structure, providing efficient and reliable technical support for objective selection and smart pig farming. Full article

Motivated by altitude-induced fluctuations in oxygen partial pressure (pO₂) and their impacts on PCS off-line arc motion and erosion response, this study proposes a comparative experimental approach featuring single-variable control under constant total pressure and coordinated multi-source electrical-signal observation. A reciprocating current-carrying arc-generation rig was established, in which pO₂ was equivalently regulated via a constant-pressure gas substitution and mixing approach. High-speed imaging–based quantitative vision analysis was integrated with synchronized voltage–current measurements to evaluate the net effects of five O₂ volumetric fraction levels (6, 11, 14, 17, and 21 vol%) under a DC supply of 120 V/25 A on arc dynamics, electrochemical processes, and contact pair erosion. Based on repeated-test results, the 14 vol% case exhibited the poorest stability (maximum fluctuation coefficient 20.306%), whereas the 17 vol% case showed the lowest current-carrying efficiency (minimum 56.070%) together with the most severe erosion damage. Moreover, with increasing pO₂, the erosion morphology evolved in a staged manner, transitioning from localized central ablation accompanied by melt-related traces to adhesive wear-induced delamination, and ultimately to electrochemical oxidative wear. Overall, pO₂ imposes a pronounced non-monotonic “window effect” on arc stability and erosion, providing key evidence for PCS structural optimization and risk assessment in open operating environments. Full article

This work presents the development and validation of a modular low-power wind turbine emulator (WTE) specifically designed for academic research and distributed generation applications. The primary objective is to provide a flexible and cost-effective test bench capable of replicating the aerodynamic and mechanical performance of a bladed rotor without the need for wind tunnels or specific field conditions. The emulator integrates a 4.5 kW three-phase induction machine as the motor and a 1 kW permanent magnet synchronous generator (PMSG). The system is managed by an ARM Cortex M7 microcontroller, which gives instructions to a Siemens Sinamics Variable Frequency Drive (VFD) that is used for torque vector control, offering superior dynamic response to wind speed variations. The aerodynamic characteristics were previously derived using blade element momentum (BEM) theory and validated using MATLAB/Simulink simulations. Unlike traditional steady-state emulators, this study addresses dynamic behavior through an autonomous control algorithm that reduces mechanical stress and compensates for inertia differences. Experimental tests conducted in a grid-connected scenario using a commercial on-grid inverter showed high correlation between the emulator’s output and the field data of a real EOLOCAL AG1000 turbine. The results confirm the system’s reliability as a platform for evaluating power conversion systems and for future expansions, such as blade pitch control emulation. Full article

The design and implementation of a dynamic frequency sweeping algorithm for a 3 kW RF power source are underpinned by theoretical principles aimed at optimizing impedance matching under pulse-mode operation. The algorithm dynamically adjusts the output frequency within a predefined range to align the source impedance Z_source with the conjugate of the load impedance Z*_load, maximizing the power transfer efficiency and minimizing the reflection coefficient Γ. This is achieved by leveraging the maximum power transfer theorem and adapting to dynamic load variations, such as those induced by the plasma state transitions. The algorithm incorporates adaptive step size adjustments based on the rate of change of Γ, predictive frequency initialization using historical data, and real-time impedance monitoring to ensure efficient convergence within the constrained pulse “ON” time (TON). Integration with pulse mode requires synchronization with the pulse signal, fast convergence, and optimized search strategies. Experimental validation on a 13.56 MHz, 3 kW Automatic Sweep Generator testbed operating at 20 kHz pulse modulation with a 50% duty cycle demonstrates a linear and stable sweep, achieving impedance matching and low reflected power within 5.0172 ms. These findings highlight the algorithm’s potential for high-precision applications, such as RF plasma excitation, and underscore the importance of adaptive techniques in dynamic RF systems. Full article