This paper presents a novel implementation of a battery charging system operating at 1.5 kV DC, employing the Virtual Synchronous Machine (VSM) algorithm to control two bidirectional DC–AC converters operating in parallel. The VSM mimics a synchronous generator's inertia and damping effects, providing grid voltage and frequency support. A VSM can be easily operated in both generating and motoring modes. Hence, it provides a useful way to control the storage devices in large-scale battery system applications, avoiding the need for a dedicated DC DC converter. The proposed VSM implementation is described in detail, and its dynamic performance in grid-connected systems is demonstrated by simulations. Moreover, operation in islanding conditions is also investigated. Due to the inherent grid-forming capability of this control strategy, the ability to supply power to loads connected to the local grid, avoid service discontinuity during grid faults, and exploit energy previously stored in battery energy storage systems (BESSs) is of great interest in the development of future smart grids. Experimental results are expected to confirm the feasibility of this approach for medium voltage applications in renewable energy storage systems (RESSs).
The integration of battery energy storage systems (BESS) in active distribution networks (ADNs) requires reliable communication networks that connect distributed energy resources (DERs) to distribution system operators (DSOs). This paper presents a co-simulation framework that combines parallelized BESS scheduling optimization, packet-level communication network modeling, and DSO supervisory commands to evaluate power–communication inter-dependencies across multi-community ADNs. The framework integrates a scalable OpenDSS-PM power distribution system model, an OMNeT++/INET communication network emulator, and a DSO federate, coupled through HELICS using a lightweight DNP3-over-TCP/IP protocol to enable time-stamped measurement and command signal exchanges. A community-level BESS scheduling approach is utilized to determine optimal charge/discharge setpoints for peak demand shaving. The framework is evaluated under three scenarios on a modified IEEE 13- bus test feeder: ideal communications, shared-network congestion, and contingency event operation. Results demonstrate how communication latency, throughput limitations, and channel utilization affect BESS scheduling, supervisory command signal responsiveness, and the overall reliable operation of the ADN. The framework enables utilities and researchers to assess communication-aware BESS scheduling strategies, exploring infrastructure requirements, and enhancing ADN operation under realistic communication network conditions.
Interleaved auxiliary-commutated resonant-pole (ACRP) inverters are used for high-precision DC–AC conversion. However, direct application of steady-state carrier phases at start up may give different units unequal first conduction intervals, causing start-up inrush current. This article analyzes the start-up inrush mechanism and proposes a soft start-up strategy based on carrier phase control. First, the relationship between the first conduction interval and the switch current peak is derived to clarify the origin of the start-up inrush current. Then, the passive current sharing after start-up is described using switching-cycle averaging, and the synchronization interval before carrier phase restoration is determined. In the proposed strategy, the corresponding carriers of the interleaved modules are first aligned so that all units start with the same first conduction interval. The carrier phases are then restored to their steady-state interleaved positions under the switch current limit. Experiments on a 300 V, 250 kHz prototype show that the peak switch current is reduced from approximately 20 A under direct start-up to the 10 A steady state envelope. The complete soft start-up process is finished within 0.2 ms. The measured passive current-sharing time constant agrees with the calculated value within 2.38%, and tests under resistive and resistive–inductive (R–L) loads verify the effectiveness of the proposed strategy.
Doubly fed induction generator wind turbine (DFIG WT) based grid-forming (GFM) control enhances active supporting capability. Nevertheless, existing research indicates GFM control reduces low-frequency band damping and causes instability issues, i.e., low frequency oscillations (LFOs) and torsional vibrations (TVs). These two issues not only influence the lifespan of GFM-DFIG drivetrains, but also may trigger the forced oscillation of synchronous generators, which lead to widespread disconnections of renewable energy sources. LFOs and TVs are located in similar frequency bands, posing challenges for analysis and suppression. To this end, a unified model is presented in this paper to analyze LFOs and TVs, which reveals the potential mechanisms of the above modes and quantitatively different parameter effects in the GFM-DFIG WT. The suppression of LFOs and TVs through power loop parameter tuning adversely affects the frequency regulation ability of DFIG WT. Accordingly, a dual-mode phase compensation controller is developed to mitigate both oscillations simultaneously. The validity of the proposed strategy is ultimately confirmed through experimental verification.
Reliable diagnosis of compound faults in wind turbine gearboxes remains a significant challenge because conventional single-sensor approaches are often unable to effectively capture the complex interactions among multiple fault characteristics under practical operating conditions. To address this limitation, this study proposes a hybrid fault diagnosis framework, termed CEKNN, for compound fault diagnosis of wind turbine gearboxes. The proposed method integrates audio, triaxial vibration, torque, and rotational speed signals, which are transformed into feature images using the Gramian Angular Summation Field. A Convolutional Extension Neural Network is employed to extract deep features and compute Extension Distances, which are subsequently fused and classified using a K Nearest Neighbor model. Experimental results demonstrate that the proposed framework achieves a diagnostic accuracy of 97.1%, outperforming conventional machine learning and single-sensor approaches. In addition, a LabVIEW-based graphical user interface is developed to provide remote visualization, anomaly warning, and intelligent fault diagnosis for gearbox systems. The proposed framework requires only 0.34 s for inference per sample following offline model training, demonstrating its computational efficiency and supporting its practical potential for real-time fault diagnosis and intelligent operation and maintenance of wind turbine gearbox systems.
Past incidents reported by the Occupational Safety and Health Administration (OSHA) indicate that arc-flash events pose a serious safety hazard to workers in DC traction systems. Meanwhile, despite documented incidents and regulations mandating arc-flash hazard assessment, existing calculation methods are either not applied or fail to account for the transient behavior of protections in these systems. This article proposes a detailed time-domain simulation platform for assessing arc-flash hazards in DC traction systems. It is based on detailed models of substation rectifiers, protective devices, and arc impedance, permitting the key characteristics of a fault in a DC traction system to be represented in the time domain. The results indicate that incident energy highly depend on the protective device functions employed and their associated settings, as well as the presence of a transfer-trip mechanism.
Local based protection schemes lack high selectivity and sensitivity for protection of smart hybrid AC-DC microgrid (HMG), due to bi-directional power flow, different fault current magnitude under both operating modes and different fault current behavior of AC and DC sub-grids. Therefore, this work proposed a logarithmic square current difference index (LSCDI) based primary unified current differential protection scheme (UCDPS) for protection of smart HMG. In the proposed approach, log operator is applied on the square of superimposed current difference obtained from line ends, and added with unity factor to avoid undefined values in steady state. The proposed approach is very sensitive and reliable to identify high resistance faults, as small variation in differential relaying current appears large, as it is expressed in terms of percentage of steady state current, and subsequently squared. The LSCDI-UCDPS is validated on modified IEEE-15-bus in MATLAB and elucidates high sensitivity (500Ω-G.C.M./400Ω-I.M.-AC, 240Ω-DC-both modes) with fast response (0.5ms-DC, within 1cycle-AC) in both operating modes for radial and ring configuration of smart HMG. It possesses high selectivity and accuracy by differentiating internal faults from external faults and system transients. It is robust for communication delay, immune to noise and measurement errors. The proposed differential scheme is successfully validated on laboratory-level hardware-in-loop setup of HMG, by using OPAL-RT-4512 controller and GE MiCOM P54C IED, having IEC 61850 protocol. The scheme is also compared with recent schemes proposed for HMG to highlight its merit and efficacy.
Heterogeneous industrial clusters such as refiners, data centers, and manufacturing facilities represent high-density loads that should maintain operational continuity during grid emergencies while providing flexible frequency recovery support. Existing strategies-conventional under-frequency load shedding, centralized, and independent controllers- either result in disproportionate energy curtailment across clusters, imposing heavy communication overhead, or are reactive in nature. This paper introduces a fault-triggered vulnerability-aware distributed Model Predictive Control (MPC) architecture in which each cluster autonomously optimizes its operation during normal conditions, and a consensus layer based on the Alternating Direction Method of Multipliers (ADMM) activates only when frequency deviates from a defined threshold. A hysteresis approach prevents spurious activation and deactivation around the threshold. The introduced vulnerability-aware mechanism results in an adaptive consensus that redistributes the burden away from the clusters approaching cascading failure limits. Voltage constraints are enforced via linearized sensitivities derived from a Newton-Raphson AC power flow. Validation across a 24-hour simulation with three heterogeneous faults, four comparison baselines, five uncertainty scenarios, and cluster counts up to 10, demonstrated that the proposed framework reduces energy curtailment by 68% and 56% relative to independent and centralized MPC, respectively. The Jain fairness index of 0.983 confirms that the proposed architecture results in optimal burden distribution across heterogeneous clusters. The ADMM coordination operates with a duty cycle of 11.2% only, and the convergence rate improves from 72% to 85% from two to ten clusters, illustrating scalability. All frequency deviations remain within operational bounds well above the under frequency relay thresholds defined in IEEE Std C37.117-2007.
Nanocrystalline alloys, featuring high saturation flux density and low hysteresis loss, have become promising core materials for high-frequency transformers. However, the magnetization dynamics and hysteresis loss formation mechanisms of nanocrystalline alloys under kHz alternating magnetic fields are still not fully understood. To address this issue, a three-dimensional micromagnetic model of FINEMET nanocrystalline alloy is established in OOMMF and validated by static magnetization characteristics and dynamic hysteresis loss experiments. Based on the validated model, the magnetization response, domain evolution, energy conversion, and damping dissipation behaviors under high-frequency sinusoidal excitation are systematically investigated. The results show that the magnetization reversal process can be divided into five dynamic stages: initial stabilization, energy pre-accumulation and magnetization relaxation, domain nucleation and early expansion, domain-wall-dominated fast reversal, and saturation approach with structural adjustment. Energy evolution and instantaneous dissipation power analyses indicate that the energy dissipation within one cycle is mainly concentrated in the stages of domain nucleation, rapid domain-wall propagation, and local structural adjustment before saturation, with the domain-wall-dominated fast reversal stage contributing the largest loss. In addition, the hysteresis loss increases with both excitation amplitude and frequency in the simulations. Under the present micromagnetic model and parameter set, a model-predicted transition region near 28 mT is observed, corresponding to the transition of the reversal mechanism from rotation-dominated behavior to domain-nucleation- and domain-wall-motion-dominated behavior. The obtained results provide theoretical support for loss analysis of high-frequency transformer cores and optimization design of magnetic materials.
This paper presents a robust sensorless control strategy for medium-voltage (MV) induction motor (IM) drives operating through long feeder cables, as encountered in subsea and harsh-environment industrial applications. In these systems, severe voltage deviations at the motor terminals and the im practicality of installing local sensors pose major challenges to conventional control schemes. To address these issues, an H2–linear matrix inequality (LMI) full-state feedback controller is developed, using a polytopic uncertainty model to ensure robustness against large cable-parameter uncertainties. The scheme includes an extended Kalman filter (EKF) to estimate all required states and the rotor speed from inverter-side measurements only. The effectiveness of the proposed method is validated through full-scale MV simulations and experimental tests on a LV laboratory setup.
With the continuous development of aircraft technology, the power density and safety of generators are required to be higher in the aircraft power system. This paper investigates a hybrid excitation starter generator with radial additional air-gaps (HESG-RAA), which has high power density, a wide flux-regulation range, and the ability to suppress short-circuit current. The topology design and equivalent magnetic circuit of the HESG-RAA are introduced. In addition, the electromagnetic performance of the investigated HESG-RAA is analyzed, including no-load characteristics, load characteristics, and short-circuit characteristics. The mechanical strength of the rotor of the HESG-RAA is analyzed at the highest speed. Finally, a 90 kW HESG prototype is manufactured, and the test results verified the effectiveness of the HESG-RAA structure and flux-regulation principle.
Overcharging in lithium-ion batteries (LIBs) is a critical safety challenge, potentially leading to thermal runaway and catastrophic failure. The current study deeply investigates the degradation mechanisms of NMC/graphite cells in overcharge conditions up to 150% State of Charge (SoC). A detailed failure progression is characterized by correlating real-time voltage and surface temperature data with periodic Electrochemical Impedance Spectroscopy (EIS) measurements. The results reveal a sequence of events starting from lithium plating, dendritic growth, and finally a minor internal short circuit (ISC). A key finding is the identification of a clear failure signature: a sudden voltage drop that occurs at approximately 128–130% SoC, which coincides with a sharp, five-fold acceleration in the cell's temperature rise rate. Analysis of the impedance data provides a microscopic validation of this process, showing an initial increase in charge transfer resistance (RCT) due to plating, followed by a collapse of RCT and a sharp increase in ohmic resistance (RS) at the moment of the ISC. A comparative analysis of two cells confirms this failure pathway's reproducibility while highlighting the stochastic nature of the final short-circuit event's severity. A simple correlated voltage-temperature gradient based detection strategy has been proposed for safety monitoring of LIBs.
Windings are the fundamental components of electric machines. Conventional winding analysis tools include slot diagrams, star diagrams, phasor diagrams, winding functions, etc. This paper presents a unified computational tool for winding analysis that encompasses the essential idea behind these con ventional methods and at the same time facilitates computations. The idea is based on the relationship among multiple concepts specifically associated with electric machine windings, including magnetic, electrical, layer, and phase orders. Such a relationship can be described through multi-dimensional arrays. Five example windings, namely fractional slot, full-pitch, short-pitch, concentric, and hairpin windings, are presented to illustrate the pro posed framework. Furthermore, various applications scenarios, including skewing, number of turns, toroidal windings, winding balance, and end winding are discussed to show its flexibility. The representation of generic windings are also given and it is used to analyze the upper bound of possible winding configurations. Lastly, the new representation enables the inverse design of windings—a step towards EM design automation. Several key inverse design algorithms are presented and validated.
Distributed photovoltaic (DPV) generation plays an important role in the economic and low-carbon operation of modern power systems. However, the widespread data incompleteness, arising from highly dispersed sites and heterogeneous measurement conditions, poses a significant challenge to the accuracy of large-scale DPV power forecasting. To achieve accurate power forecasting with incomplete data, we extend DPV clustering using the Probability Mass Similarity Kernel (PMK) and propose a Geo-PMK dual-scale dynamic clustering method. DPV stations are initially clustered based on geographic location, followed by dynamic refinement using PMK to capture time-series similarity. From each resulting sub-cluster, a representative station is selected to characterize regional power output for forecasting. Subsequently, a Prior-Gated and Posterior-Calibrated Mixture of Experts (PP-MoE) ensemble forecasting framework is proposed. This framework dynamically integrates heterogeneous experts via a gating mechanism that fuses prior information with posterior error calibration, utilizing Top-K sparse routing for efficient forecasting. Finally, the forecasting results are scaled by capacity ratios and aggregated to obtain the total regional output. The effectiveness of the proposed method is validated using real-world data from 274 stations in Lanzhou, China. Results demonstrate that this method can maintain robustness under incomplete data scenarios and significantly improve the regional power forecasting accuracy of large-scale DPV systems.
Continuousoperation of power systems is critical to modern society, even during unexpected or emergency situations. The core method to evaluate the security of power systems is contingency analysis, a computationally-intensive process which considers how a set of potential scenarios would affect the electrical grid. This paper presents a robust security-evaluation method which enables a topology- and parameter-free estimation of an existing system-level security metric, the system aggregate megawatt contingency overload (SysAMWCO), by leveraging a deep-learning-based approach. Using the results of conventional AC-based contingency analysis for a simulated large-scale power system, a deep neural network (DNN) is trained to estimate SysAMWCO values from only bus loads, generator setpoints, and branch-connection statuses as inputs. Although DC-based analysis only considers fewer than 200 contingencies after screening (down from over 5,000), it is outperformed by this DNN framework with 10-times lower error and 30-times faster SysAMWCO evaluation time. Given these results, this framework demonstrates significant promise as a computationally-efficient surrogate for DC-based contingency-analysis and power-security evaluation in high-fidelity simulation environments with a clear pathway for future validation against industry-operational datasets.
In this study, we investigated methods to reduce carbon particles including soot in exhaust gas emissions and black carbon particles in the atmosphere. We evaluated novel designs of surface dielectric barrier discharge (SDBD) units, which have a set of discharge electrodes on their surface. We tested three types of connections with different discharge and particle incineration characteristics, including a type with a floating sub-electrode, one in which an identical voltage was applied to both the main and the sub-electrode, and another in which an external inductor and a capacitor were added. In the experimental evaluation, we compared the performance of these units in reducing carbon and analyzed the electrical behaviors of the sub-electrode using an equivalent circuit model.
Particulate matter concentrations in underground subway tunnels are often elevated due to mechanical wear and limited natural ventilation, posing potential health risks to passengers and workers. This study developed and field-validated a two-stage brush-type electrostatic precipitator integrated into subway ventilation systems to mitigate PM concentrations under real operating conditions. The system was installed in both supply- and exhaust-type ventilation rooms across interconnected tunnel sections. The ESP achieved removal efficiencies exceeding 70–90% for PM2.5 and PM10 in one pass operation, and its performance varied according to ambient particle size characteristics. Integration of the system into supply-type ventilation rooms resulted in removal of approximately 50–60% of incoming outdoor PM, corresponding to in-tunnel PM2.5 concentration reductions of 15–32% despite dynamic airflow and train induced turbulence. In exhaust-type configurations, removal efficiencies ranged from 66% to 71%, and treated exhaust air exhibited lower PM concentrations than the surrounding ambient atmosphere. The results demonstrate that integration of brush-type ESP systems into subway ventilation infrastructure can effectively reduce particulate concentrations within tunnels while limiting the emission of tunnel-derived particles to the urban environment.
The electrification of maritime electrical power systems has progressed from isolated onboard upgrades to integrated shipboard power systems, shore-to-ship interfaces, and port microgrids operating under strict safety, reliability, and regulatory constraints. In parallel, artificial intelligence, machine learning, and advanced control techniques have been increasingly reported in literature to enhance energy management, resilience, and operational efficiency. However, their technical suitability and deployment readiness remain insufficiently assessed from a system-level and operational perspective. This paper presents a system-level technical assessment of control, optimization, and AI/ML-based approaches for maritime power systems, with exclusive focus on IEEE Transactions on Industry Applications. Using a rigorously selected dataset of forty-two journal papers, the study classifies methods by maritime system context, operational layer, and method role, audits formulation transparency, and evaluates deployment realism under non-ideal operational assumptions. To support more structured comparison, a semi-quantitative rubric is used to summarize how explicitly formulation and deployment realism criteria are addressed across method classes. The results show that optimization and model predictive control dominate safety-critical decision layers due to explicit constraint enforcement, while learning-based methods are limited to auxiliary roles such as forecasting, monitoring, and decision support. The rubric-based comparison further indicates that optimization, MPC, and hybrid AI–optimization approaches achieve the strongest formulation scores, whereas supportive learning-based methods remain weaker in deployment realism because sensing non-idealities, communication constraints, and real-world feasibility are rarely treated explicitly.
