Search Results (44)

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

With the development of modern power systems, AC distribution networks face increasing demands for supply flexibility and reliability. Energy storage-based soft open points (E-SOPs), which integrate energy storage systems into the DC side of traditional SOP connecting AC distribution networks, not only maintain power flow control capabilities but also enhance system supply performance, providing a novel approach to AC distribution network fault recovery. To fully leverage the advantages of E-SOPs in handling faults in flexible interconnected AC distribution networks (FIDNs), this paper proposes an E-SOP-based FIDN islanding recovery method. First, the basic structure and control modes of SOPs for AC distribution networks are elaborated, and the E-SOP-based AC distribution network structure is analyzed. Second, with maximizing total load recovery as the objective function, the constraints of E-SOPs are comprehensively considered, and recovery priorities are established based on load importance classification. Then, a multi-dimensional improvement of the dung beetle optimizer (DBO) algorithm is implemented through Logistic chaotic mapping, adaptive parameter adjustment, elite learning mechanisms, and local search strategies, resulting in an efficient solution for AC distribution network power supply restoration. Finally, the proposed FIDN islanding partitioning and fault recovery methods are validated on a double-ended AC distribution network structure. Simulation results demonstrate that the improved DBO (IDBO) algorithm exhibits a superior optimization performance and the proposed method effectively enhances the load recovery capability of AC distribution networks, significantly improving the self-healing ability and operational reliability of AC distribution systems. Full article

Polyphenylene sulfide (PPS)-based separators have garnered significant attention as high-performance components for next-generation lithium-ion batteries (LIBs), driven by their exceptional thermal stability (>260 °C), chemical inertness, and mechanical durability. This review comprehensively examines advances in PPS separator design, focusing on two structurally distinct categories: porous separators engineered via wet-chemical methods (e.g., melt-blown spinning, electrospinning, thermally induced phase separation) and nonporous solid-state separators fabricated through solvent-free dry-film processes. Porous variants, typified by submicron pore architectures (<1 μm), enable electrolyte-mediated ion transport with ionic conductivities up to >1 mS·cm⁻¹ at >55% porosity, while their nonporous counterparts leverage crystalline sulfur-atom alignment and trace electrolyte infiltration to establish solid–liquid biphasic conduction pathways, achieving ion transference numbers >0.8 and homogenized lithium flux. Dry-processed solid-state PPS separators demonstrate unparalleled thermal dimensional stability (<2% shrinkage at 280 °C) and mitigate dendrite propagation through uniform electric field distribution, as evidenced by COMSOL simulations showing stable Li deposition under Cu particle contamination. Despite these advancements, challenges persist in reconciling thickness constraints (<25 μm) with mechanical robustness, scaling solvent-free manufacturing, and reducing costs. Innovations in ultra-thin formats (<20 μm) with self-healing polymer networks, coupled with compatibility extensions to sodium/zinc-ion systems, are identified as critical pathways for advancing PPS separators. By addressing these challenges, PPS-based architectures hold transformative potential for enabling high-energy-density (>500 Wh·kg⁻¹), intrinsically safe energy storage systems, particularly in applications demanding extreme operational reliability such as electric vehicles and grid-scale storage. Full article

The subway power supply system, as a critical component of urban rail transit infrastructure, plays a pivotal role in ensuring operational efficiency and safety. However, current systems remain heavily dependent on manual interventions for fault diagnosis and recovery, limiting their ability to meet the growing demand for automation and efficiency in modern urban environments. While the concept of “self-healing” has been successfully implemented in power grids and distribution networks, adapting these technologies to subway power systems presents distinct challenges. This review introduces an innovative approach by integrating multi-agent systems (MASs) with advanced artificial intelligence (AI) algorithms, focusing on their potential to create fully autonomous self-healing control architectures for subway power networks. The novel contribution of this review lies in its hybrid model, which combines MASs with the IEC 61850 communication standard to develop fault diagnosis, isolation, and recovery mechanisms specifically tailored for subway systems. Unlike traditional methods, which rely on centralized control, the proposed approach leverages distributed decision-making capabilities within MASs, enhancing fault detection accuracy, speed, and system resilience. Through a thorough review of the state of the art in self-healing technologies, this work demonstrates the unique benefits of applying MASs and AI to address the specific challenges of subway power systems, offering significant advancement over existing methodologies in the field. Full article

To achieve a hierarchical and quantitative evaluation of grid-wide power quality in the distribution network, reflecting the overall power quality level of the distribution network, a comprehensive evaluation method for power quality in a grid-wide system based on complex network theory is proposed. Firstly, based on the propagation characteristics of power quality disturbances, a power quality evaluation index system is constructed. Secondly, to reflect the constraint effect of the local power quality level of nodes on the overall power quality level of the distribution system, corresponding indices such as improved node degree, improved node electrical betweenness, and node self-healing capability are proposed based on complex network theory, and the power quality influence degree of nodes is calculated. Then, the GRA-ANP (Grey Relational Analysis–Analytic Network Process) subjective weight calculation method is improved by introducing grey relational analysis to address the impact of differences in different decision-making results. Based on power quality monitoring data, the entropy weight method is used for objective weighting. To avoid the partiality of a single weight evaluation result, the game equilibrium algorithm is employed to calculate the comprehensive weight of each power quality index. Subsequently, considering the correlation and dependency among indices, the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method is used to obtain the power quality grade of each node. Combining this with the calculation of the power quality influence degree of nodes, the overall power quality grade of the distribution network is determined, achieving a hierarchical and quantitative evaluation of power quality in the entire distribution system. Finally, through a case study analysis of an improved 13-node distribution network, it is verified that the proposed method can fully extract data information and produce comprehensive and accurate power quality assessment results by comparing it with other methods. This provides strong support for the safe and stable operation of the distribution system and the subsequent optimization and management of power quality. Full article

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS. Full article

A problem with applying machine learning for analyzing power system dynamics is the lack of specific datasets. In this realm, defining a strong methodology to obtain power system dynamic data is an important task prior to the application of any machine learning tool. This is particularly important considering the current growing research in the field of self-healing grids. Thus, this paper presents a well-defined stochastic methodology that can be used to generate dynamic data that can afterwards be analyzed using machine learning tools. The proposed method is based on a Monte Carlo simulation and this paper presents the procedure to perform it. Full article

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements. Full article

The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems. Full article

Given the various aspects of climate change and the growing demand for energy, energy efficiency and environmental protection have become major concerns worldwide. If not taken care of, energy demand will become unmanageable due to technological growth in cities and nations. The solution to the global energy crisis could be an advanced two-way digital power flow system that is capable of self-healing, interoperability, and predicting conditions under various uncertainties and is equipped with cyber protections against malicious attacks. The smart grid enables the integration of renewable energy sources such as solar, wind, and energy storage into the grid. Therefore, the perception of the smart grid and the weight given to it by researchers and policymakers are of utmost importance. In this paper, the studies of many researchers on smart grids are examined in detail. Based on the literature review, various principles of smart grids, the development of smart grids, functionality of smart grids, technologies of smart grids with their characteristics, communication of smart grids, problems in the implementation of smart grids, and possible future studies proposed by various researchers have been presented. Full article

The large-scale development of distributed generators poses challenges to the operation of distribution networks in remote areas. The power grid structure of the regional distributed networks in remote areas is relatively weak. When a fault occurs in the connecting channel, the regional distributed network may be separated from the main grid and become an island. Those distributed generators not only cannot maintain stable operation of the island but may lead to reclosing failure. This may lead to power interruption of important loads. Aiming at the demand for continuous power supply of important loads in regional distributed networks, a self-healing control method based on the cooperation of source-load-storage is proposed in this paper. The characteristics of the regional distributed network are analyzed first. A real-time island stability control method based on the operating conditions combined with regulation with tripping is proposed. This method comprehensively evaluates the self-healing ability of the regional distributed network after the island. The regulation strategy is adopted preferentially to realize the stable operation of the island without losing any load. When the regulation ability is insufficient, the strategy combines regulation and tripping. Considering the importance of load and source-load ratio, and according to the real-time power of each feeder, the optimal feeders are cut off. The purpose of island stability control of the regional distributed network was achieved. In this way, the load loss is minimized, and the speed of island re-connection to the grid is also accelerated. The seamless power supply of important loads in a regional distributed network is guaranteed. A complex Hardware-in-Loop simulation test platform of RTDS is built to verify the correctness of the proposed method. Full article

To investigate the effects of epoxy resin at low content on the physical properties, rheological properties, and self-healing properties of asphalt, epoxy asphalts with epoxy resin contents of 2%, 5%, 10%, and 20% were prepared. The distribution of epoxy asphalt (EA) in epoxy resin (ER) was quantitatively studied by fluorescence microscopy (FM) to investigate the feasibility of the preparation process. The glass transition temperature of epoxy asphalt was quantitatively analyzed by the differential thermal analyzer (DSC). The physical properties of epoxy asphalt were characterized by penetration test, ductility test, and softening point test. The rheological properties of epoxy asphalt were analyzed by the dynamic shear rheometer (DSR) to evaluate the self-healing properties of epoxy asphalt. The results show that the epoxy resin could be uniformly distributed in the asphalt, as verified by fluorescence microscopy (FM). With the increase in epoxy resin content, the glass transition temperature of epoxy asphalt gradually decreases, and the epoxy asphalt with 20% content shows the lowest glass transition temperature. At the same time, epoxy resin gives asphalt a higher modulus and high temperature performance, and the penetration and softening point of epoxy asphalt has also been greatly improved. On the contrary, the three-dimensional cross-linked grid structure, which is formed by epoxy resin and curing agent, reduces the rheological properties of epoxy asphalt and increases the elastic components of epoxy asphalt. Although the maltenes diagram still exhibits typical viscoelastic characteristic, the flow behavior index and flow activation energy of epoxy asphalt decreased. Full article

3D bioprinting holds great potential for use in tissue engineering to treat degenerative joint disorders, such as osteoarthritis. However, there is a lack of multifunctional bioinks that can not only support cell growth and differentiation, but also offer protection to cells against injuries caused by the elevated oxidative stress; this conditions is a common characteristic of the microenvironment of the osteoarthritis disease. To mitigate oxidative stress-induced cellular phenotype change and malfunction, an anti-oxidative bioink derived from an alginate dynamic hydrogel was developed in this study. The alginate dynamic hydrogel gelated quickly via the dynamic covalent bond between the phenylboronic acid modified alginate (Alg-PBA) and poly (vinyl alcohol) (PVA). It presented good self-healing and shear-thinning abilities because of the dynamic feature. The dynamic hydrogel supported long-term growth of mouse fibroblasts after stabilization with a secondary ionic crosslinking between introduced calcium ions and the carboxylate group in the alginate backbone. In addition, the dynamic hydrogel showed good printability, resulting in the fabrication of scaffolds with cylindrical and grid structures with good structural fidelity. Encapsulated mouse chondrocytes maintained high viability for at least 7 days in the bioprinted hydrogel after ionic crosslinking. Most importantly, in vitro studies implied that the bioprinted scaffold could reduce the intracellular oxidative stress for embedded chondrocytes under H₂O₂ exposure; it could also protect the chondrocytes from H₂O₂-induced downregulation of extracellular matrix (ECM) relevant anabolic genes (ACAN and COL2) and upregulation of a catabolic gene (MMP13). In summary, the results suggest that the dynamic alginate hydrogel can be applied as a versatile bioink for the fabrication of 3D bioprinted scaffolds with an innate antioxidative ability; this technique is expected to improve the regenerative efficacy of cartilage tissues for the treatment of joint disorders. Full article

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes. Full article

During the autumn season, Sicily is often affected by severe weather events, such as self-healing storms called V-shaped storms. These phenomena cause significant total rainfall quantities in short time intervals in localized spatial areas. In this framework, this study analyzes the meteorological event recorded on 11–12 November 2019 in Sicily (southern Italy), using the Weather Research and Forecasting (WRF) model with a horizontal spatial grid resolution of 3 km. It is important to note that, in this event, the most significant rainfall accumulations were recorded in eastern Sicily. In particular, the weather station of Linguaglossa North Etna (Catania) recorded a total rainfall of 293.6 mm. The precipitation forecasting provided by the WRF model simulation has been compared with the data recorded by the meteorological stations located in Sicily. In addition, a further simulation was carried out using the Four-Dimensional Data Assimilation (FDDA) technique to improve the model capability in the event reproduction. In this regard, in order to evaluate which approach provides the best performance (with or without FDDA), the Root Mean Square Error (RMSE) and dichotomous indexes (Accuracy, Threat Score, BIAS, Probability of Detection, and False Alarm Rate) were calculated. Full article