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Keywords = fault self-healing

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20 pages, 1996 KiB  
Article
Low-Voltage Power Restoration Based on Fog Computing Load Forecasting and Data-Driven Wasserstein Distributionally Robust Optimization
by Ruoxi Liu, Yifan Song, Yuan Gui, Hanqi Dai, Zhiyong Wang, Chengdong Yin, Qinglei Qin, Wenqin Yang and Yue Wang
Energies 2025, 18(8), 2096; https://doi.org/10.3390/en18082096 - 18 Apr 2025
Viewed by 239
Abstract
This paper proposes a fault self-healing recovery strategy for passive low-voltage power station areas (LVPSAs). Firstly, being aware of the typical structure and communication conditions of the LVPSAs, a fog computing load forecasting method is proposed based on a dynamic aggregation of incremental [...] Read more.
This paper proposes a fault self-healing recovery strategy for passive low-voltage power station areas (LVPSAs). Firstly, being aware of the typical structure and communication conditions of the LVPSAs, a fog computing load forecasting method is proposed based on a dynamic aggregation of incremental learning models. This forecasting method embeds two weighted ultra-short-term load forecasting techniques of complementary characteristics and mines real-time load to learn incrementally, and thanks to this mechanism, the method can efficiently make predictions of low-voltage loads with trivial computational burden and data storage. Secondly, the low-voltage power restoration problem is overall formulated as a three-stage mixed integer program. Specifically, the master problem is essentially a mixed integer linear program, which is mainly intended for determining the reconfiguration of binary switch states, while the slave problem, aiming at minimizing load curtailment constrained by power flow balance along with inevitable load forecast errors, is cast as mixed integer type-1 Wasserstein distributionally robust optimization. The column-and-constraint generation technique is employed to expedite the model-resolving process after the slave problem with integer variables eliminated is equated with the Karush–Kuhn–Tucker conditions. Comparative case studies are conducted to demonstrate the performance of the proposed method. Full article
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95 pages, 2088 KiB  
Review
Integration of Multi-Agent Systems and Artificial Intelligence in Self-Healing Subway Power Supply Systems: Advancements in Fault Diagnosis, Isolation, and Recovery
by Jianbing Feng, Tao Yu, Kuozhen Zhang and Lefeng Cheng
Processes 2025, 13(4), 1144; https://doi.org/10.3390/pr13041144 - 10 Apr 2025
Viewed by 1023
Abstract
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 [...] Read more.
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
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12 pages, 2134 KiB  
Article
A Self-Healing WDM Access Network with Protected Fiber and FSO Link Paths Effective Against Fiber Breaks
by Tsu-Hsin Wu, Chien-Yu Liao, Chien-Hung Yeh, Yuan-Wen Chen, Yu-Hsin Kao, Sung-Yi Lin, Yu-Heng Lin and Shien-Kuei Liaw
Photonics 2025, 12(4), 323; https://doi.org/10.3390/photonics12040323 - 30 Mar 2025
Viewed by 249
Abstract
In this article, an additional protected fiber and free-space optical (FSO) link path is proposed, to provide self-healing capabilities for protection against fiber faults in wavelength division multiplexed passive optical network (WDM-PON) systems. The new optical line terminal (OLT), remote node (RN), and [...] Read more.
In this article, an additional protected fiber and free-space optical (FSO) link path is proposed, to provide self-healing capabilities for protection against fiber faults in wavelength division multiplexed passive optical network (WDM-PON) systems. The new optical line terminal (OLT), remote node (RN), and optical network unit (ONU) in the presented PON architecture result in self-protective function against fiber breakpoints. In the measurement, 25 Gbit/s on-off keying (OOK) modulation was applied on each WDM channel to assess the downstream and upstream signals after 25 km single-mode fiber (SMF) and 25 km SMF + 2 m FSO connections, respectively. In addition to using protected fiber paths for self-healing operations. This PON system can also apply the FSO link method. The measured bit error rate (BER) for all downstream and upstream traffic was maintained below 3.8 × 10−3 with forward error correction (FEC). The detected optical power sensitivity of the proposed self-restorative fiber- and FSO-based WDM-PON for downstream and upstream WDM signals ranged from −33.5 to −28.5 dBm and from −33 to −28.5 dBm, respectively, and the corresponding power budgets of the downstream and upstream WDM signals were between 29.5 and 30.5 dB and 33 and 38 dB, respectively. Full article
(This article belongs to the Special Issue Free-Space Optical Communication and Networking Technology)
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15 pages, 2416 KiB  
Article
Research on Self-Diagnosis and Self-Healing Technologies for Intelligent Fiber Optic Sensing Networks
by Ruiqi Zhang, Liang Fan and Dongzhu Lu
Sensors 2025, 25(6), 1641; https://doi.org/10.3390/s25061641 - 7 Mar 2025
Viewed by 716
Abstract
To address the issue of insufficient reliability of fiber optic sensing networks in complex environments, this study proposes a self-diagnosis and self-healing method based on intelligent algorithms. This method integrates redundant fiber paths and a fault detection mechanism, enabling rapid data transmission recovery [...] Read more.
To address the issue of insufficient reliability of fiber optic sensing networks in complex environments, this study proposes a self-diagnosis and self-healing method based on intelligent algorithms. This method integrates redundant fiber paths and a fault detection mechanism, enabling rapid data transmission recovery through redundant paths during network faults, ensuring the stable operation of the monitoring system. Unlike traditional self-diagnosis techniques that rely on an optical time domain reflectometer, the proposed self-diagnosis algorithm utilizes data structure analysis, significantly reducing dependence on costly equipment and improving self-diagnosis efficiency. On the hardware front, a light switch driving device that does not require an external power source has been developed, expanding the application scenarios of optical switches and enhancing system adaptability and ease of operation. In the experiments, three fiber optic sensing network topologies—redundant ring structure, redundant dual-ring structure, and redundant mesh structure—are constructed for testing. The results show that the average self-diagnosis time is 0.1257 s, and the self-healing time is 0.5364 s, validating the efficiency and practicality of the proposed method. Furthermore, this study also proposes a robustness evaluation model based on sensor perception ability and coverage uniformity indicators, providing a theoretical basis for the self-healing capability of fiber optic sensing networks. This model aids in network topology optimization and fault recovery strategy design, contributing to the improvement of the stability and reliability of fiber optic sensing networks in practical applications. Full article
(This article belongs to the Special Issue Advances in Optical Fiber Sensors and Fiber Lasers)
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17 pages, 2478 KiB  
Article
A Flexible Interconnected Distribution Network Power Supply Restoration Method Based on E-SOP
by Lin Jiang, Canbin Wang, Wei Qiu, Hui Xiao and Wenshan Hu
Energies 2025, 18(4), 954; https://doi.org/10.3390/en18040954 - 17 Feb 2025
Viewed by 496
Abstract
To enhance the self-healing control capability of soft open points with energy storage (E-SOPs) and optimize the fault recovery performance in flexible interconnected distribution networks, this paper proposes a novel power supply restoration method based on E-SOP. The methodology begins with a comprehensive [...] Read more.
To enhance the self-healing control capability of soft open points with energy storage (E-SOPs) and optimize the fault recovery performance in flexible interconnected distribution networks, this paper proposes a novel power supply restoration method based on E-SOP. The methodology begins with a comprehensive analysis of the E-SOP’s fundamental architecture and loss model. Subsequently, a dual-objective optimization function is formulated to maximize the sum of nodal active load restoration while minimizing network losses. The optimization problem is transformed into a second-order cone programming formulation under comprehensive operational constraints. To solve this complex optimization model, an innovative hybrid approach combining the Improved Whale Optimization Algorithm (IWOA) with second-order cone programming is developed. The proposed methodology is extensively validated using the IEEE 33-node test system. The experimental results demonstrate that this approach significantly enhances the power supply restoration capability of distribution networks while maintaining practical feasibility. Full article
(This article belongs to the Special Issue Measurement Systems for Electric Machines and Motor Drives)
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37 pages, 1800 KiB  
Article
Hierarchical Resources Management System for Internet of Things-Enabled Smart Cities
by Christoforos Papaioannou, Asimina Dimara, Alexios Papaioannou, Ioannis Tzitzios, Christos-Nikolaos Anagnostopoulos and Stelios Krinidis
Sensors 2025, 25(3), 616; https://doi.org/10.3390/s25030616 - 21 Jan 2025
Viewed by 1080
Abstract
The efficient management of IoT systems is fundamental to advancing smart cities while enabling the seamless integration of technologies that enhance urban sustainability and resilience. This paper introduces a Hierarchical Resource Management System (HRMS) tailored for IoT-enabled smart cities, emphasizing a decentralized architecture [...] Read more.
The efficient management of IoT systems is fundamental to advancing smart cities while enabling the seamless integration of technologies that enhance urban sustainability and resilience. This paper introduces a Hierarchical Resource Management System (HRMS) tailored for IoT-enabled smart cities, emphasizing a decentralized architecture at the building level and scaling up to city-wide applications. At its core, the system integrates the Adaptive Resilient Node (ARN), designed to autonomously manage energy resources and ensure continuous operation through self-healing capabilities. This study outlines the HRMS architecture, operational workflows, and core functionalities, demonstrating how the hierarchical framework supports real-time decision-making, fault tolerance, and scalable resource allocation. The proposed system’s lightweight inter-node communication enhances workload balancing and system responsiveness, addressing critical challenges in urban energy management. Experimental evaluations show that the system achieves up to a 50% improvement in energy efficiency and a 30% reduction in downtime across various urban environments, highlighting its transformative potential for sustainable and resilient urban growth. Full article
(This article belongs to the Section Internet of Things)
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15 pages, 4933 KiB  
Article
An Improved Secondary Control Strategy for Dynamic Boundary Microgrids toward Resilient Distribution Systems
by Yijun Wang, Jiaju Shi, Nan Ma, Guowei Liu, Lisheng Xin, Ziyu Liu, Dafu Liu, Ziwen Xu and Chen Chen
Energies 2024, 17(7), 1731; https://doi.org/10.3390/en17071731 - 4 Apr 2024
Viewed by 1272
Abstract
In order to achieve the flexible and efficient utilization of distributed energy resources, microgrids (MGs) can enhance the self-healing capability of distribution systems. Conventional primary droop control in microgrids exhibits deviations in voltage and frequency and lacks research on voltage–frequency control during network [...] Read more.
In order to achieve the flexible and efficient utilization of distributed energy resources, microgrids (MGs) can enhance the self-healing capability of distribution systems. Conventional primary droop control in microgrids exhibits deviations in voltage and frequency and lacks research on voltage–frequency control during network reconfiguration. Therefore, this paper investigates the control strategy of secondary control for voltage and frequency during the process of reconstructing distribution networks to operate in the form of microgrids after faults. Firstly, the mathematical model of three-phase voltage-source inverters with droop control is analyzed. Then, inspired by the generator’s secondary frequency control strategy, an improved secondary control method based on droop control is proposed, and the effectiveness of the improved droop control strategy is verified. Finally, simulation examples of distribution networks with various dynamic boundary topology changes are designed to simulate a relatively stable control result, validating the proposed strategy. Full article
(This article belongs to the Section A1: Smart Grids and Microgrids)
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17 pages, 16590 KiB  
Article
Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example
by Xiaoke Huang and Chunjie Yang
Electronics 2024, 13(5), 845; https://doi.org/10.3390/electronics13050845 - 22 Feb 2024
Viewed by 1531
Abstract
Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to [...] Read more.
Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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19 pages, 1806 KiB  
Article
Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks
by Ahmed Sami Alhanaf, Hasan Huseyin Balik and Murtaza Farsadi
Energies 2023, 16(22), 7680; https://doi.org/10.3390/en16227680 - 20 Nov 2023
Cited by 25 | Viewed by 5730
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Fuel Cell Renewable Hybrid Power Systems 2021)
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17 pages, 3916 KiB  
Article
A Self-Healing Strategy for Modern Distribution Networks
by Cleberton Reiz, Caio E. M. Pereira and Jonatas B. Leite
Energies 2023, 16(16), 5890; https://doi.org/10.3390/en16165890 - 9 Aug 2023
Cited by 2 | Viewed by 1740
Abstract
Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A [...] Read more.
Electrical distribution companies have been investing in modernizing their structures, especially operation automation. The integration of information technologies and communications makes fast power restoration during fault events, providing better profit to companies and a more reliable and safe distribution network for customers. A self-healing strategy can be implemented for protection and control devices to work cooperatively, achieving the global purpose of automatic distribution system restoration. Thus, this work proposes a methodology for short-circuit fault detection, isolation of the faulted section, and restoration of downstream sections using neighbor feeders. The protection devices use standardized IEC and ANSI/IEEE functions to sensitize faults in the system and to promote adequate isolation, allowing the consequent restorative process. A genetic algorithm optimizes the devices’ parameters used in the protection scheme, making fastest the isolation process and ensuring the protection system coordination and selectivity. Results obtained using Simulink® allows for verifying the proposed methodology’s behavior and efficiency. Full article
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54 pages, 12312 KiB  
Review
A Tutorial on Agricultural IoT: Fundamental Concepts, Architectures, Routing, and Optimization
by Emmanuel Effah, Ousmane Thiare and Alexander M. Wyglinski
IoT 2023, 4(3), 265-318; https://doi.org/10.3390/iot4030014 - 27 Jul 2023
Cited by 7 | Viewed by 4891
Abstract
This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via a systematic survey and synthesis of the related literature. The negative impacts of climate change and the [...] Read more.
This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via a systematic survey and synthesis of the related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on the specific performance and user expectations as well as technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault-tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a three-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks. Full article
(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)
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42 pages, 4766 KiB  
Review
Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools
by Obinna Johnphill, Ali Safaa Sadiq, Feras Al-Obeidat, Haider Al-Khateeb, Mohammed Adam Taheir, Omprakash Kaiwartya and Mohammed Ali
Future Internet 2023, 15(7), 244; https://doi.org/10.3390/fi15070244 - 17 Jul 2023
Cited by 18 | Viewed by 7656
Abstract
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 [...] Read more.
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
(This article belongs to the Section Smart System Infrastructure and Applications)
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16 pages, 6527 KiB  
Article
Fault Seamless Self-Healing Method of Regional Distributed Network Based on the Cooperation of Source-Load-Storage
by Chengzhi Wei, Chunming Tu, Weiwei Song and Fan Xiao
Sustainability 2023, 15(10), 8099; https://doi.org/10.3390/su15108099 - 16 May 2023
Viewed by 1339
Abstract
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 [...] Read more.
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
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41 pages, 4319 KiB  
Review
Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review
by Zakarya Oubrahim, Yassine Amirat, Mohamed Benbouzid and Mohammed Ouassaid
Energies 2023, 16(6), 2685; https://doi.org/10.3390/en16062685 - 13 Mar 2023
Cited by 20 | Viewed by 3944
Abstract
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 [...] Read more.
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
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40 pages, 1804 KiB  
Review
Proactive Self-Healing Approaches in Mobile Edge Computing: A Systematic Literature Review
by Olusola Adeniyi, Ali Safaa Sadiq, Prashant Pillai, Mohammed Adam Taheir and Omprakash Kaiwartya
Computers 2023, 12(3), 63; https://doi.org/10.3390/computers12030063 - 13 Mar 2023
Cited by 6 | Viewed by 4822
Abstract
The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry’s growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer [...] Read more.
The widespread use of technology has made communication technology an indispensable part of daily life. However, the present cloud infrastructure is insufficient to meet the industry’s growing demands, and multi-access edge computing (MEC) has emerged as a solution by providing real-time computation closer to the data source. Effective management of MEC is essential for providing high-quality services, and proactive self-healing is a promising approach that anticipates and executes remedial operations before faults occur. This paper aims to identify, evaluate, and synthesize studies related to proactive self-healing approaches in MEC environments. The authors conducted a systematic literature review (SLR) using four well-known digital libraries (IEEE Xplore, Web of Science, ProQuest, and Scopus) and one academic search engine (Google Scholar). The review retrieved 920 papers, and 116 primary studies were selected for in-depth analysis. The SLR results are categorized into edge resource management methods and self-healing methods and approaches in MEC. The paper highlights the challenges and open issues in MEC, such as offloading task decisions, resource allocation, and security issues, such as infrastructure and cyber attacks. Finally, the paper suggests future work based on the SLR findings. Full article
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