Search Results (1,387)

Accurate and real-time monitoring of low-frequency electromagnetic field (EMF) is essential in power and industrial environments, yet most conventional approaches still suffer from limited spatial coverage, manual operation, and insufficient digitization. To address these challenges, this paper proposes an intelligent EMF monitoring system that integrates six-axis magnetic field sensing, temperature compensation, vector synthesis, Sub-1 GHz wireless communication, and real-time data visualization. The system supports simultaneous measurement of both AC and DC magnetic fields across the 30 Hz–100 kHz range, with specific optimization for power-frequency conditions (50/60 Hz). Designed with modular integration and low power consumption, it is suitable for portable deployment in field scenarios. Comprehensive laboratory and substation tests demonstrate high accuracy, with maximum measurement errors of 1.17% under zero-field and 1.42% under applied-field conditions—well below the ±5% tolerance defined by international standards. Wireless performance tests further confirm stable long-distance communication, achieving ranges of up to 5 km without significant transmission errors, while overall system measurement error reached as low as 0.015%. These results verify the system’s robustness, fidelity, and compliance with international safety standards. Overall, the proposed platform provides a practical and scalable solution for intelligent EMF monitoring, offering strong potential for deployment in industrial environments and infrastructure-critical applications. Full article

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids. Full article

In regions with concentrated load centers in China, the AC transmission network is dense, leading to challenges such as difficulties in power flow control and excessive short-circuit currents. The scale effect of AC grids is approaching saturation, making it imperative to develop new AC/DC hybrid grid structures. To enhance the controllability, security, and stability of AC/DC hybrid power systems, a single-AC multi-DC hybrid grid structure is proposed in this paper. The operational characteristics of this grid are analyzed in terms of power flow control capability, N-1 overload, short-circuit current, frequency stability, voltage stability, and synchronous stability, and a method for constructing the single-AC multi-DC hybrid grid is presented. Finally, simulation analysis is conducted on a typical single-AC multi-DC case, and the results indicate that this hybrid grid structure can simultaneously satisfy the controllability, security, and stability requirements of AC/DC power systems, making it a highly promising grid configuration. Full article

In tokamak-based nuclear fusion systems, powering the coils to control the plasma is a challenge that involves design choices that are a mix between advanced and traditional approaches. Each tokamak coil requires peculiar driving conditions and needs specific design activities. This paper deals with power supply design assessment for the Divertor (DIV) Coils in the Divertor Tokamak Test (DTT) facility. The design constraints of high-current (5500 A) and relatively low-voltages lead to the comparison of an SCR-based AC–AC converter (cycloconverter) with an IGBT-based DC–AC inverter with devices in a parallel solution and with interleaved modulation. The design assessment of two converter solutions to drive the DIV coils with the control issues were explored and described. Several simulation results were carried out to define the DIV coils operative conditions. Furthermore, an electro-thermal analysis on the used IGBT or thyristor devices was carried out considering the losses and the highest temperatures obtained in the conditions of maximum stress for the components. Full article

Electric fields (EFs) have emerged as effective, non-chemical tools for modulating microbial populations in complex matrices such as wastewater. This review consolidates current advances on EF-induced alterations in microbial structures and functions, focusing on both vegetative cells and spores. Key parameters affected include membrane thickness, transmembrane potential, electrical conductivity, and dielectric permittivity, with downstream impacts on ion homeostasis, metabolic activity, and viability. Such bioelectrical modifications underpin EF-based detection methods—particularly impedance spectroscopy and dielectrophoresis—which enable rapid, label-free, in situ microbial monitoring. Beyond detection, EFs can induce sublethal or lethal effects, enabling selective inactivation without chemical input. This review addresses the influence of field type (DC, AC, pulsed), intensity, and exposure duration, alongside limitations such as species-specific variability, heterogeneous environmental conditions, and challenges in achieving uniform field distribution. Emerging research highlights the integration of EF-based platforms with biosensors, machine learning, and real-time analytics for enhanced environmental surveillance. By linking microbiological mechanisms with engineering solutions, EF technologies present significant potential for sustainable water quality management. Their multidisciplinary applicability positions them as promising components of next-generation wastewater monitoring and treatment systems, supporting global efforts toward efficient, adaptive, and environmentally benign microbial control strategies. Full article

Microgrid systems play a vital role in managing distributed energy resources like solar, wind, batteries, and supercapacitors. However, maintaining stable AC/DC bus voltages and minimizing grid reliance under dynamic conditions is challenging. Traditional control methods such as Sliding Mode Controllers (SMCs) suffer from issues like chattering and slow convergence, reducing practical effectiveness. This paper proposes a hybrid AC/DC microgrid that operates in both grid-connected and islanded modes while ensuring voltage stability and efficient energy use. A Conditional-Based Super-Twisting Sliding Mode Controller (CBSTSMC) is employed to address the limitations of conventional SMCs. The CBSTSMC enhances system performance by reducing chattering, improving convergence speed, and offering better tracking and disturbance rejection. To further refine controller performance, an Improved Grey Wolf Optimization (IGWO) algorithm is used for gain tuning, resulting in enhanced system robustness and precision. An Energy Management System (EMS) is integrated to intelligently regulate power flow based on renewable generation and storage availability. The proposed system is tested in real time using a Texas Instruments Delfino C2000 microcontroller through a Controller-in-the-Loop (CIL) setup. The simulation and hardware results confirm the system’s ability to maintain stability and reliability under diverse operating scenarios, proving its suitability for future smart grid applications. Full article

As a key component for maintaining the efficient and stable operation of flexible DC transmission systems, the arm reactor often suffers from uneven loss distribution and localized overheating in its windings due to the superimposed AC and DC currents, which adversely affects its operational lifespan. Furthermore, arm reactors are frequently deployed in offshore environments for long-distance, high-capacity power transmission, imposing additional requirements on energy utilization efficiency and seismic resistance. To address these challenges, this study proposes an optimization design method for arm reactors based on a triple-constraint mechanism of “equal resistive voltage–equal loss density–equal encapsulation temperature rise,” aiming to achieve “low loss–low temperature rise–low weight.” First, an equivalent electromagnetic model of the arm reactor under combined AC and DC operating conditions is established to analytically calculate the self- and mutual-inductance-distribution characteristics between winding layers and the loss distribution across windings. The calculated losses are then applied as heat sources in a fluid–thermal coupling method to compute the temperature field of the arm reactor. Next, leveraging a Kriging surrogate model to capture the relationship between the winding temperature rise in the bridge-arm reactor and the loss density, encapsulation width, encapsulation height, and air duct width, the revised analytical expression reduces the temperature rise error from 43.74% to 11.47% compared with the traditional empirical formula. Finally, the triple-constraint mechanism of “equal resistive voltage–equal loss density–equal encapsulation temperature rise” is proposed to balance interlayer current distribution, suppress total loss generation, and limit localized hotspot formation. A prototype constructed based on the optimized design demonstrates a 44.51% reduction in total loss, a 39.66% decrease in hotspot temperature rise, and a 24.83% reduction in mass while maintaining rated inductance, validating the effectiveness of the proposed design algorithm. Full article

This paper presents an intelligent communication architecture, designed to manage multiple power devices operating within a shared Low-Voltage Direct Current (LVDC) bus. These devices act either as energy consumers, e.g., Electric Vehicle (EV) chargers, Power Distribution Units (PDUs), or as sources and regulators, e.g., Alternating Current-to-Direct Current (AC/DC) converters, energy storage system (ESS) units. Communication is established using industrial protocols such as Modular Digital Bus (MODBUS) over Transmission Control Protocol (TCP) or Remote Terminal Unit (RTU), and Controller Area Network (CAN). The proposed system supports both data acquisition and configuration of field devices. It exposes their information to an Energy Management System (EMS) via a MODBUS TCP server. A key contribution of this work is the integration of a lightweight Machine Learning (ML)-based data prioritization mechanism that dynamically adjusts the update frequency of each MODBUS parameter based on its current relevance. This ML-based method has been prototyped and evaluated within a virtualized Internet of Things (IoT) gateway environment. It enables real-time, efficient, and scalable communication without altering the EMS or disrupting legacy protocol operations. Furthermore, the proposed approach allows for early testing and validation of the prioritization strategy before full hardware integration in the demonstrators planned as part of the SHIFT2DC project under the Horizon Europe program. Full article

The increasing penetration of electrified vehicles is accelerating the evolution of on-board and off-board charging systems, which must deliver higher efficiency, power density, safety, and bidirectionality under increasingly demanding constraints. This article presents a system-level review of state-of-the-art charging architectures, with a focus on galvanically isolated power conversion stages, wide-bandgap-based switching devices, battery pack design, and real-world implementation trends. The analysis spans the full energy path—from grid interface to battery terminals—highlighting key aspects such as AC/DC front-end topologies (Boost, Totem-Pole, Vienna, T-Type), high-frequency isolated DC/DC converters (LLC, PSFB, DAB), transformer modeling and optimization, and the functional integration of the Battery Management System (BMS). Attention is also given to electrochemical cell characteristics, pack architecture, and their impact on OBC design constraints, including voltage range, ripple sensitivity, and control bandwidth. Commercial solutions are examined across Tier 1–3 suppliers, illustrating how technical enablers such as SiC/GaN semiconductors, planar magnetics, and high-resolution BMS coordination are shaping production-grade OBCs. A system perspective is maintained throughout, emphasizing co-design approaches across hardware, firmware, and vehicle-level integration. The review concludes with a discussion of emerging trends in multi-functional power stages, V2G-enabled interfaces, predictive control, and platform-level convergence, positioning the on-board charger as a key node in the energy and information architecture of future electric vehicles. Full article

This systematic review examines the combined use of electroencephalography (EEG) and transcranial electrical stimulation (tES) in both clinical and healthy populations. The review focuses on EEG’s role in guiding, monitoring, and evaluating tES interventions and assesses the generalizability of EEG responses to different tES protocols. A comprehensive search across Google Scholar, PubMed, Scopus, IEEE Xplore, ScienceDirect, and Web of Science identified 162 relevant studies using the query: “EEG AND (tDCS OR transcranial direct current stimulation OR tACS OR transcranial alternating current stimulation OR tRNS OR transcranial random noise stimulation OR tPCS OR transcranial pulsed current stimulation)”. Quality was assessed using the Quality Assessment Tool for Quantitative Studies (QATQS). Most studies used EEG post tES to assess neuromodulatory effects, with fewer studies using EEG for protocol design or incorporating real-time EEG for adaptive stimulation. Some studies integrated EEG both before and after stimulation, but considerable heterogeneity in tES parameters and EEG metrics limited reproducibility and comparability. Many studies reported non-significant EEG changes despite standardized approaches. Methodological quality was generally low, and the link between EEG changes and clinical outcomes remains unclear. The findings underscore the potential of EEG-informed, personalized tES protocols, though the use of real-time closed-loop systems remains a limited approach in current research. Full article

This paper aims to unveil the symmetry–asymmetry transition mechanisms in transient fault waveforms of offshore wind power AC/DC transmission systems, addressing the critical limitation of traditional simulation methods of the fact that they cannot characterize the dynamic evolution of system symmetry, such as static impedance adjustment failing to capture transient asymmetry caused by parameter imbalance or converter control. It proposes a fault waveform simulation approach integrating mechanism analysis, scenario extraction, and model optimization. Key contributions include clarifying the quantitative links between key system parameters like submarine cable capacitance and inductance and symmetry–asymmetry characteristics, defining the transient decay rate oscillation frequency and voltage peak as core indicators to quantify symmetry breaking intensity; classifying typical fault scenarios into a symmetry-breaking type with synchronous three-phase imbalance and a persistent asymmetry type with zero-sequence and negative-sequence distortion based on symmetry evolution dynamics and revising grid-connection test indices such as lowering the low-voltage ride-through threshold and specifying the voltage type for different test objectives; and constructing a simplified embedded RLC second-order model with symmetry–asymmetry constraints to reproduce the whole process of symmetric steady state–fault symmetry breaking–recovery symmetry reconstruction. Simulation results verify the method’s effectiveness, with symmetry indicator reproduction errors ≤ 5% and asymmetric feature fitting goodness R² ≥ 0.92, which confirms that the method can effectively reveal the symmetry–asymmetry mechanisms of offshore wind power fault transients and provides reliable technical support for improving offshore wind power fault simulation accuracy and grid-connection test reliability, laying a theoretical basis for the grid-connection testing of offshore wind turbines and promoting the stable operation of offshore wind power systems. Full article

Distribution networks are facing increasing challenges due to the growing share of renewable energy sources (RESs), particularly because of the volatile nature of the available power. In addition to targeted grid expansion measures, the concept of a dynamic grid topology offers an additional layer of flexibility in the power system. Furthermore, there are concepts to use active coupling methods in distribution grids, such as medium-voltage direct current (MVDC) systems, which enable horizontal power flows between distribution grids and thus active control. This paper investigates the potential of combining dynamic passive and active coupling between two distribution grids. Particle swarm optimization (PSO) is used to determine both an optimized operating point of two MVDC interconnections as well as the most efficient switch configuration within both networks. The goal of the optimization is to reduce both network losses and power exchange between the different voltage levels. To evaluate its potential, various use cases are simulated using a representative feed-in of photovoltaics while considering grid constraints. Individual and combined impacts of dynamic AC switching and DC coupling are compared using a modified IEEE-123 test feeder. The results show a significant optimization potential, especially with an increase in RES penetration within the grid. In the best scenarios, the power losses can be decreased by 33.73% and the power transfer can be reduced by 8.75%. Full article

Partial discharge (PD) is a critical indicator of insulation degradation in high-voltage DC systems, necessitating accurate diagnosis to ensure long-term reliability. Conventional AC-based diagnostic methods, such as phase-resolved partial discharge analysis (PRPDA), are ineffective under DC conditions, emphasizing the need for waveform-based analysis. This study presents a novel clustering framework for DC PD pulses, leveraging multi-level wavelet decomposition and statistical feature extraction. Each signal is decomposed into multiple frequency bands, and 70 distinctive waveform features are extracted from each pulse. To mitigate feature redundancy and enhance clustering performance, principal component analysis (PCA) is employed for dimensionality reduction. Experimental data were obtained from multiple defect types and measurement distances using a 22.9 kV cross-linked polyethylene (XLPE) cable system. The proposed method significantly outperformed conventional time-frequency (T-F) mapping techniques, particularly in scenarios involving signal attenuation and mixed noise. Propagation-induced distortion was effectively addressed through multi-resolution analysis. In addition, field noise sources such as HVDC converter switching transients and fluorescent lamp emissions were included to assess robustness. The results confirmed the framework’s capability to distinguish between multiple PD types and noise sources, even in challenging environments. Furthermore, optimal mother wavelet selection and correlation-based feature analysis contributed to improved clustering resolution. This framework supports robust PD classification in practical HVDC diagnostics. The framework can contribute to the development of real-time autonomous monitoring systems for HVDC infrastructure. Future research will explore incorporating temporal deep learning architectures for automated PD-type recognition based on clustered data. Full article

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids. Full article

In order to meet the needs of grid integration of various renewable energy sources and promote long-distance power transmission, a hybrid multi-infeed DC system architecture consisting of a line-commutated converter (LCC) and a modular multilevel converter (MMC) is constructed. Focusing on the issue of traditional differential protection refusing to operate under high-resistance grounding faults and failing under symmetrical faults, a dual-criteria protection mechanism is proposed in this paper. By integrating current differential and voltage criterion, the accurate identification of various types of AC line faults can be realized. A hybrid DC system simulation model was built on MATLAB, the sampled data was decoupled, and the differential quantity was calculated to test the dual-criteria protection mechanism. The simulation results show that the proposed protection mechanism can effectively identify various faults within the hybrid DC multi-feed system area and faults outside the area and has robustness to complex working conditions such as high-resistance grounding and three-phase short circuits, which improves the sensitivity, selectivity, and adaptability of the protection. This method is designed for AC line protection under the disturbance of multi-infeed DC systems. It is not directly applicable to pure DC microgrids. The concept can be extended to AC/DC hybrid microgrids by adding DC-side protection criteria and re-calibrating thresholds. Full article