Search Results (246)

The expansion and ongoing refinement of control solutions for three-phase microgrids are key enablers in the transition from conventional distribution networks to smart microgrids. By integrating distributed generation, a microgrid can operate in either grid-connected or island mode. One of the major technical challenges in microgrid operation is mitigating or eliminating phase power unbalances. Unbalanced single-phase loads, combined with unbalanced and intermittent single-phase generation, can produce adverse effects on both energy efficiency and power quality. Unlike conventional distribution networks, microgrids may exhibit bidirectional power flows, which can occur simultaneously on all phases or differ from phase to phase. This paper introduces new analytical expressions for sizing a balancing capacitive compensator (BCC) for three-phase four-wire systems and derives a simplified sizing algorithm. The approach is validated through a numerical study using a Matlab/Simulink model of a low-voltage three-phase microgrid with high penetration of single-phase loads and single-phase distributed sources. The BCC is installed at the point of common coupling (PCC) between the microgrid and the main grid. Three operating regimes (cases) of the microgrid were analyzed, considering three compensation scenarios (sub-cases) for each: 1—without compensation, 2—with balanced capacitive compensation (classical), and 3—with unbalanced capacitive compensation (with BCC). For each of the three regimes (cases), the use of the BCC determines, at the PCC, in addition to the cancellation of the reactive component of the positive sequence current, the cancellation of the negative- and zero-sequence currents. In other words, the BCC–microgrid assembly is seen from the main grid either as a perfectly balanced active power load or as a perfectly balanced active power source. Thus, the BCC prevents the propagation of the unbalance disturbance in the main grid; in the considered case study, this also results from the cancellation of the negative- and zero-sequence components of the phase voltages measured at the PCC. The results show that the load-balancing capability of the BCC can be extended to power-flow balancing in any network section, including cases where the phase power directions differ. Implemented as a BCC-type SVC or as an automatically adjustable variant (ABCC), the proposed unbalanced shunt capacitive compensation method is effective for mitigating or eliminating bidirectional phase power-flow unbalances. Full article

Power system blackouts remain a major concern for modern electricity networks, as they often result from cascading failures that lead to substantial load shedding and widespread service disruptions. This paper presents a dynamic resilience assessment of hybrid AC/DC power systems and investigates the effectiveness of voltage-source-converter-based high-voltage direct current (VSC-HVDC) technology in enhancing system resilience under outage contingencies. The study contributes by integrating protection devices and their settings into the analysis and by providing a quantitative evaluation of the system response to N-2 and N-3 contingencies using PSS^®E simulations. The demand not served index is used as a measure of resilience, and its cumulative distribution functions are computed to compare the performance of AC and DC interconnections. The results underscore the importance of VSC-HVDC links in mitigating cascading failures, highlighting their potential as a resilience-enhancing component in modern power grids. Full article

The electrification of mobility increases the need for efficient local distribution and charging infrastructures. In this context, direct current (DC) architectures may reduce conversion stages, transmission losses, and material demand compared with alternating current (AC) systems. This study aims to quantify the environmental implications of AC- and DC-based grid and charging infrastructures for large-scale rollout in Germany. For this purpose, a dynamic life-cycle assessment (DLCA) is conducted for parking garages, parcel centers, and delivery bases over the period 2023–2045, covering the production and use phases with respect to global warming potential (GWP) and material demand. The results show that DC configurations achieve lower total GWP across all application contexts investigated. For parking garages, DC reduces total GWP by 9.3% compared with AC, while for parcel logistics facilities the reduction amounts to 5.7%. Copper is identified as the dominant material driver, and DC reduces copper demand by 17.1–58.7% depending on the application. A screening-based supply-risk assessment further indicates the elevated relevance of copper due to rising demand and Germany’s import dependence. Overall, the findings provide quantitative evidence that DC-based infrastructures can reduce both environmental impacts and copper demand in large-scale charging infrastructure deployment. Full article

While the generation mechanisms of high-frequency oscillations caused by voltage source converter-based high-voltage direct current (VSC-HVDC) systems have been widely investigated, their propagation paths and spatial influence within the power grid remain largely unexplored. To address this critical gap, this paper proposes a novel oscillation propagation analysis method based on branch high-frequency active power distribution. First, from the perspective of equivalent impedance, the mechanism of high-frequency oscillation caused by the VSC-HVDC system in a single-machine system is elaborated. Then, mathematical modeling and theoretical derivations reveal that synchronous generators primarily act as passive impedances at high frequencies and that transmission lines significantly distort high-frequency voltage and current amplitudes. Crucially, high-frequency active power remains inherently stable and immune to these line distortion effects. Building upon these characteristics, an instantaneous power calculation method using broadband measurement data is derived to trace the propagation path. Comprehensive case studies using a 4-machine 2-area system and the New England 10-machine 39-bus system demonstrate that the proposed method can accurately map actual physical propagation paths, evaluate an oscillation’s influence range, and reliably locate a high-frequency oscillation’s source. Full article

A photovoltaic (PV) and battery-based energy system can provide the necessary and sufficient electric power to off-grid power system networks due to the technological advancements in both performance improvement and lower system cost. The absence of reactive power in direct current (DC) power system networks has several advantages over corresponding alternating current (AC) power system networks. In this paper, we have investigated a case study for the PV farm coupled with a battery energy storage system (BESS) as a stand-alone power system network in the Red Sea New City, Kingdom of Saudi Arabia. The study consists of two cases, which are the DC battery coupling configuration for the AC power network system and the end-to-end DC (EEDC) configuration for the power network system. Using the same size of solar PV farm and battery storage, we have compared the performance of the two case configurations of different power system networks after thirty years of operation. The results show that implementing the EEDC power system network has a major advantage in improved energy efficiency of the power system (directly related to cost-effectiveness) and lower capital investment of the power system that includes electric power generation, transmission, distribution, and utilization for all applications, including artificial intelligence-based data centers. Full article

This study proposes a three-dimensional forward modeling framework for geoelectric current distribution under high-voltage direct current (HVDC) monopolar operation. The proposed approach is based on a multi-resolution resistor network (MR-RN) discretization, in which gradient fusion interpolation is employed to suppress flux discontinuities at coarse–fine interfaces, and exterior equivalent boundary resistors are introduced to approximate open boundaries, enabling efficient and stable large-scale three-dimensional forward modeling. Compared with the traditional structured grid and finite element method (FEM), the proposed MR-RN achieves comparable accuracy while reducing computation time by up to 96% and the number of degrees of freedom by two orders of magnitude. Case studies on layered Earth, boundary extension, and substation–field coupling demonstrate that the MR-RN model maintains errors within 1–3%, confirming its suitability for large-scale HVDC ground return simulations and geoelectric safety assessment. Full article

Achieving reliable, grid-scale electricity generation from nuclear fusion, as envisioned by the DEMOnstration Fusion Power Plant (DEMO) and future commercial reactors, requires unprecedented plasma stability and long-term control. This operational goal is fundamentally challenged by, among others, the dynamic nature of the high temperature plasma and the need to monitor high-Z impurities, such as tungsten, which can severely compromise energy confinement, resulting in discharge disruption and damage to internal reactor walls. Real-time Soft X-ray (SXR) diagnostic systems are therefore an integral and critical component of fusion power plant infrastructure, providing essential temporal and spatial resolution data on these fast-evolving phenomena. To address the severe demands imposed by the extreme operating environment of future fusion reactors, such as DEMO (including intense neutron and gamma fluxes), this work details a current stage in the long-term development of an advanced and robust diagnostic system engineered specifically for technological preparation and future application in these high-fluence environments. This paper presents the third generation of the SXR measurement system, GEM3k, based on Gas Electron Multiplier (GEM) technology. This novel diagnostic utilizes a Field Programmable Gate Array (FPGA)-based architecture, specifically designed for the high-rate acquisition of energy- and spatially resolved plasma radiation distributions. The GEM3k design exploits the inherent radiation hardness of GEM detectors, positioning them as robust sensor units for monitoring plasma dynamics and impurity emissions in future fusion environments. The system readout comprises approximately 34,000 individual pixels mapped to nearly 3000 measurement channels in an XYUV coordinate configuration. This layout enables submillimeter spatial resolution simultaneously with a time resolution better than 10 ms. Addressing the engineering challenges of such a complex high-density readout, this work details the comprehensive design of the GEM3k system, focusing on its architecture, electronics, performance estimations, and data distribution strategies. By enabling precise tracking of impurities and fast plasma behavior, the GEM3k system contributes to the stable, high-gain operation required for future fusion reactors. This directly supports the development of sustainable fusion energy and its eventual integration into modern electricity grids. Furthermore, the planned enhancement to a real-time operating mode could pave a way for a next-generation system for direct integration into reactor control loops. Currently in the prototype phase with initial hardware tests completed, the GEM3k design leverages our extensive experience with diagnostics developed for the JET and WEST tokamaks. Full article

The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energy storage systems. Addressing these challenges requires intelligent, adaptive, and physically consistent control strategies capable of operating under uncertain environmental and load conditions. This study proposes a Physics-Informed Neural Network (PINN)-based charge allocation framework that explicitly embeds physical constraints—namely charge conservation and State-of-Charge (SoC) equalization—directly into the learning process, enabling real-time adaptive control under varying irradiance and load conditions. The proposed controller exploits real-time measurements of PV voltage, current, and irradiance to achieve optimal charge distribution while ensuring converter stability and balanced battery operation. The framework is implemented and validated in MATLAB/Simulink under Standard Test Conditions of 1000 W·m⁻² irradiance and 25 °C ambient temperature. Simulation results demonstrate stable PV voltage regulation within the 230–250 V range, an average PV power output of approximately 95 kW, and effective duty-cycle control within the range of 0.35–0.45. The system maintains balanced three-phase grid voltages and currents with stable sinusoidal waveforms, indicating high power quality during steady-state operation. Compared with conventional Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC) methods, the PINN-based approach achieves faster SoC equalization, reduced transient fluctuations, and more than 6% improvement in overall system efficiency. These results confirm the strong potential of physics-informed intelligent control as a scalable and reliable solution for smart PV–battery energy systems, with direct relevance to renewable microgrids and electric vehicle charging infrastructures. Full article

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article

High-voltage (HV) bushings are critical hub components in power systems, whose operational reliability is paramount to the safety and stability of transmission and distribution infrastructure. Conventional management paradigms are hampered by challenges such as information silos, reactive maintenance, and imprecise condition assessment, rendering them in-adequate for the evolving demands of modern power systems. Digital twin technology, by creating a high-fidelity, re-al-time interplay between physical entities and their virtual counterparts, provides a revolutionary pathway toward the intelligent full-life-cycle management (FLCM) of HV bushings. This paper presents a review of the current state of research in this domain. It begins by reviewing research on the construction a five-dimensional digital twin framework that encompasses the entire lifecycle: design, manufacturing, operation and maintenance (O&M), and decommissioning. Subsequently, it delves into the application paradigms of digital twins across typical scenarios, including external insulation design, intelligent condition assessment, insulation defect identification, fault diagnosis, and predictive maintenance. The paper then examines the core technological underpinnings, such as multi-physics coupled modeling, multi-source heterogeneous data fusion, and data-driven model updating and condition assessment. Finally, it identifies current challenges related to data, models, standards, and costs, and offers a forward-looking perspective on future research directions, including group digital twins, deep integration with artificial intelligence, edge-side deployment, and standardization initiatives. This work aims to provide a theoretical reference and technical guidance for advancing the intelligent O&M of HV bushings and bolstering grid security. Full article

Pumped storage power stations provide essential benefits to power grids by cutting peak loads, filling valleys, and boosting renewable energy integration rates. They serve as the foundation for developing a new power system based on renewable energy. Pump turbines are currently evolving to provide higher heads, larger capacities, and higher rotating speeds. The performance and dependability of its basic components have a direct impact on the safety and stability of unit operation. Based on this, this research looks into the modal characteristics and structural aspects of essential stationary components, such as the pump-turbine head cover. By comparing the mechanical performance of two different structural designs (Design A and Design B), Design B features an overall thickness 1.5 times that of Design A and incorporates an upper flange structure. Its design aims to enhance the component’s resistance to bending and deformation, optimize stress distribution while reducing peak stress values, and improve modal characteristics. This approach elevates the overall structural performance of the fixed components to accommodate the complex operating conditions of ultra-high-head pumped storage units. It was discovered that Design B had greater bending and deformation resistance than Design A, as well as better stress distribution and lower maximum stress values. This study further indicates that variations in structural design lead to significant differences in modal characteristics and overall structural performance. In particular, the thicknesses of the head cover’s main body and stiffening ribs are critical parameters that govern the modal behavior and structural properties of stationary components. These insights provide critical technical guidance for optimizing the design of stationary parts, such as the head cover, in pumped storage power plant units. Full article

To address the problems of low inversion accuracy and poor noise resistance in pulsed eddy current (PEC) grounding grid depth detection, this study proposes a novel inversion model (IE-CBiLSTM). This model integrates the Informer Encoder with the CNN-BiLSTM for the first time to detect the depth of the PEC grounding grid and conducts experimental verification based on an independently designed pulsed eddy current detection device and a dedicated coil sensor. The model design employs a two-dimensional convolutional neural network (CNN) to extract local spatial features, combines a bidirectional long short-term memory network (Bi-LSTM) to model temporal dependencies, and introduces a multi-head attention mechanism along with the Informer structure to enhance the expression of key features. In terms of data construction, the design integrates both forward simulation data and measured data to improve the model’s generalization capability. Experimental validation includes self-burial experiments and field tests at a substation. In the self-burial test, the IE-CBiLSTM inversion results show high consistency with actual burial depths under various conditions (1.0 m, 1.2 m, and 1.5 m), significantly outperforming other optimization algorithms, achieving a coefficient of determination (R²) of 0.861, along with root mean square error (E_RMS) and mean relative error (E_MR) values of 17.54 Ω·m and 0.061 Ω·m, respectively. In the field test, the inversion results also closely match the design depths from engineering drawings, with an R² of 0.933, E_RMS of 11.30 Ω·m, and E_MR of 0.046 Ω·m. These results are significantly better than those obtained using traditional Occam and LSTM methods. At the same time, based on the inversion results, a three-dimensional inversion map of the grounding grid and a buried depth profile were drawn, and the spatial direction and buried depth distribution of the underground flat steel were clearly displayed, proving the visualization ability of the model and its engineering practicality under complex working conditions. This method provides an efficient and reliable inversion strategy for deep PEC nondestructive testing of grounding grid laying. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments can span many kilometers and are loaded with challenges such as limited to no communication, hazardous terrain, blocked passages due to collapses, and vertical structures. The time-sensitive nature of these operations inherently requires solutions that are reliably deployable in practice. Moreover, a human-supervised multi-robot team is required to ensure that mobility and cognitive capabilities of various agents are leveraged for efficiency of the mission. Therefore, this article attempts to propose a solution that is suited for both air and ground vehicles and is adapted well for information sharing between different agents. This article first details a sampling-based autonomous exploration solution that brings significant improvements with respect to the current state of the art. These improvements include relying on an occupancy grid-based sample-and-project solution to terrain assessment and formulating the solution-search problem as a constraint-satisfaction problem to further enhance the computational efficiency of the planner. In addition, the demonstration of the exploration planner by team MARBLE at the DARPA Subterranean Challenge finals is presented. The inevitable interaction of heterogeneous autonomous robots with human operators demands the use of common semantics for reasoning across the robot and human teams making use of different geometric map capabilities suited for their mobility and computational resources. To this end, the path planner is further extended to include semantic mapping and decision-making into the framework. Firstly, the proposed solution generates a semantic map of the exploration environment by labeling position history of a robot in the form of probability distributions of observations. The semantic reasoning solution uses higher-level cues from a semantic map in order to bias exploration behaviors toward a semantic of interest. This objective is achieved by using a particle filter to localize a robot on a given semantic map followed by a Partially Observable Markov Decision Process (POMDP)-based controller to guide the exploration direction of the sampling-based exploration planner. Hence, this article aims to bridge an understanding gap between human and a heterogeneous robotic team not just through a common-sense semantic map transfer among the agents but by also enabling a robot to make use of such information to guide its lower-level reasoning in case such abstract information is transferred to it. Full article

The accuracy of numerical model outputs strongly depends on the quality of the initial wind field, yet ground observation data are typically sparse and provide incomplete spatial coverage. More importantly, many current mainstream correction models rely on reanalysis grid datasets like ERA5 as the true value, which relies on interpolation calculation, which directly affects the accuracy of the correction results. To address these issues, we propose a new deep learning model, PPWNet. The model directly uses sparse and discretely distributed observation data as the true value, which integrates observation point positions and a physical consistency term to achieve a high-precision corrected wind field. The model design is inspired by biological intelligence. First, observation point positions are encoded as input and observation values are included in the loss function. Second, a parallel dual-branch DenseInception network is employed to extract multi-scale grid features, simulating the hierarchical processing of the biological visual system. Meanwhile, PPWNet references the PointNet architecture and introduces an attention mechanism to efficiently extract features from sparse and irregular observation positions. This mechanism reflects the selective focus of cognitive functions. Furthermore, this paper incorporates physical knowledge into the model optimization process by adding a learned physical consistency term to the loss function, ensuring that the corrected results not only approximate the observations but also adhere to physical laws. Finally, hyperparameters are automatically tuned using the Bayesian TPE algorithm. Experiments demonstrate that PPWNet outperforms both traditional and existing deep learning methods. It reduces the MAE by 38.65% and the RMSE by 28.93%. The corrected wind field shows better agreement with observations in both wind speed and direction, confirming the effectiveness of incorporating position information and a physics-informed approach into deep learning-based wind field correction. Full article