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Shafique, Ayesha, Guo Cao, Muhammad Aslam, Muhammad Asad, and Dengpan Ye. "Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)." Sensors 20, no. 12 (2020): 3521. http://dx.doi.org/10.3390/s20123521.
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The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.
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Lai, Luyao, Dandan Li, Yating Zhang, et al. "Abnormal Dynamic Reconfiguration of Multilayer Temporal Networks in Patients with Bipolar Disorder." Brain Sciences 14, no. 9 (2024): 935. http://dx.doi.org/10.3390/brainsci14090935.
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Background: Multilayer networks have been used to identify abnormal dynamic reconfiguration in bipolar disorder (BD). However, these studies ignore the differences in information interactions between adjacent layers when constructing multilayer networks, and the analysis of dynamic reconfiguration is not comprehensive enough; Methods: Resting-state functional magnetic resonance imaging data were collected from 46 BD patients and 54 normal controls. A multilayer temporal network was constructed for each subject, and inter-layer coupling of different nodes was considered using network similarity. The promiscuity, recruitment, and integration coefficients were calculated to quantify the different dynamic reconfigurations between the two groups; Results: The global inter-layer coupling, recruitment, and integration coefficients were significantly lower in BD patients. These results were further observed in the attention network and the limbic/paralimbic and subcortical network, reflecting reduced temporal stability, intra- and inter-subnetwork communication abilities in BD patients. The whole-brain promiscuity was increased in BD patients. The same results were observed in the somatosensory/motor and auditory network, reflecting more functional interactions; Conclusions: This study discovered abnormal dynamic interactions of BD from the perspective of dynamic reconfiguration, which can help to understand the pathological mechanisms of BD.
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Lotfi, Hossein, Mohammad Ebrahim Hajiabadi, and Hossein Parsadust. "Power Distribution Network Reconfiguration Techniques: A Thorough Review." Sustainability 16, no. 23 (2024): 10307. http://dx.doi.org/10.3390/su162310307.
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Distribution network reconfiguration (DNR) plays a vital role in enhancing network sustainability by optimizing its topology. This process achieves key objectives such as reducing power losses, improving voltage profiles, balancing loads, and increasing network reliability, aligning with sustainability metrics. Depending on the goals and equipment available, reconfiguration may be applied for short-term or long-term durations. Long-term or static reconfiguration suits both conventional switches and traditional as well as modern networks. In modern networks equipped with remote-control switches, however, reconfiguration can be implemented rapidly to meet specific operational objectives. This study provides a comprehensive review of recent advancements in network reconfiguration, categorizing methods into four groups: heuristic, metaheuristic, conventional, and modern approaches. Each category is broadly defined and compared, with applications discussed for both static and dynamic reconfiguration. Dynamic reconfiguration is highlighted as a key area for future exploration in smart and modern distribution networks. This article serves as a resource for engineers and researchers, helping them select the most suitable method based on network equipment and performance goals.
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Duong-Tran, Duy, Kausar Abbas, Enrico Amico, et al. "A morphospace of functional configuration to assess configural breadth based on brain functional networks." Network Neuroscience 5, no. 3 (2021): 666–88. http://dx.doi.org/10.1162/netn_a_00193.
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Abstract The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.
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Mainali, Sabin, Akhileshwor Mishra, and Shahbuddin Khan. "Dynamic Reconfiguration of Distribution Networks Considering the Real-time Topology Variation." Journal of Advanced College of Engineering and Management 9 (November 14, 2024): 287–99. http://dx.doi.org/10.3126/jacem.v9i1.71454.
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Electrical distribution networks undergo dynamic transformations due to continuous alteration and variation of loads. These alterations necessitate engineering studies aimed at optimizing the distribution networks. Reconfiguring networks stands as a critical analysis process essential for enhancing and managing distribution systems (DSs). When starting with a stable initial DS, the distribution feeders can be reconfigured by adjusting switch statuses to enhance operational performance. In this study, we introduce a dynamic reconfiguration approach that takes into account real-time variations in the initial topology. This methodology integrates dynamic topology analysis and network reconfiguration to address real-time distribution network optimization issues.. We identify the best configuration to reduce power losses and improve the voltage profile of the real-time distribution network in accordance with network reconfiguration. A new approach to dynamic reconfiguration in distribution systems (DS) is introduced, focusing on real-time changes in the initial topology rather than a fixed network structure. This method restores DS network connectivity by dynamically analyzing topology when changes in the initial configuration are identified. By aligning with the current network structure, the proposed method determines the optimal configuration for minimizing power loss and enhancing voltage profiles. As a result, this approach can be readily applied to the real-time variation in network topology during the reconfiguration of distribution systems.
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Hoang, Nghia Le. "Optimization algorithm for reconfiguration process of the IP over optical networks." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2003): 83–91. http://dx.doi.org/10.26636/jtit.2003.3.187.
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The IP over optical (IPO) network is becoming one of the most interesting among all proposed models of transport networks nowadays. In an IPO network, the reconfiguration capability of the network could be used in order to balance the load of its network elements (NEs). Reconfiguration operations (i.e., switching in OXC nodes and rerouting in IP routers) take place in real-time. Consequently, intensive changes in NEs settings might cause failures in the existing connections in the network. For that reason, changes in NEs settings should be coordinated in a reconfiguration process. In this paper, the author has proposed an optimization method for such reconfiguration process. The mathematical model of the method including computation results has been presented.
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Fathur Nureza Aksan and Samsurizal. "Studi Rekonfigurasi Sistem Distribusi Pada Jaringan 20 kV Dengan Metode Simple Branch Exchange Pada Penyulang Cempaka." EPSILON: Journal of Electrical Engineering and Information Technology 19, no. 2 (2021): 45–52. http://dx.doi.org/10.55893/epsilon.v19i2.64.
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The increasing electric energy consumption must be balanced with good electricity network quality. To overcome this, we need a distribution network reconfiguration. Reconfiguration in power distribution network is done to improve power distribution network quality. Problem that exist in the radial distribution network is power losses. To minimize the power losses can be overcome by compensating capacitor. Done by determining the location of capacitors in distribution networks. With that the capacitor compensates the distribution network is more effective and efficient in distributing power. In this research discusses the network reconfiguration using simple branch exchange method to reduce power losses in radial distribution networks. The reconfiguration method was carried out with the help of the Electrical Transient Analysis Program (ETAP) 12.6.0 software and has been tested by PT REKAYASA INDUSTRI and PLN Area South Kalimantan on the 20 kV distribution system of Cempaka feeder, South Kalimantan. With this reconfiguration, the voltage drop that occurs on several buses, especially bus 37 and 44, has a voltage drop of up to 95.03% to 95.31% with an average drop voltage of 95.1%, which means the average voltage drop. each feeder is ableto send electricity to consumers properly.
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Simamora, Yoakim, Sigit Sukmajati, and Rio Afrianda. "OPTIMASI REKONFIGURASI JARINGAN DISTRIBUSI RADIAL UNTUK MEMINIMALKAN RUGI JARINGAN MENGGUNAKAN METODE SIMPLE BRANCH EXCHANGE." Energi & Kelistrikan 10, no. 2 (2019): 102–11. http://dx.doi.org/10.33322/energi.v10i2.218.
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The increasing electric energy consumption must be balanced with good electricity network quality. To overcome this, we need a distribution network reconfiguration. Reconfiguration in power distribution network is done to improve power distribution network quality. Problem that exist in the radial distribution network is power losses. To minimize the power losses can be overcome by compensating capacitor. Done by determining the location of capacitors in distribution networks. With that the capacitor compensates the distribution network is more effective and efficient in distributing power.
 In this research discusses the network reconfiguration using simple branch exchange method to reduce power losses in radial distribution networks . Plant that used in this final project is IEEE 33 bus feeders. Simple branch exchange method is a kind of heuristic method . It works by selecting the order of the loop is done for distribution networks reconfiguration. 
 Simulation results show that the use of a simple branch exchange method for reconfiguring network can reduce energy loss by 63,4677 kW. From the simulation results using the distribution system IEEE 33 bus feeders, showed that the decrease of energy losses from 202,6845 kW become 139,2168 kW.
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Lee, Changhyeok, Cong Liu, Sanjay Mehrotra, and Zhaohong Bie. "Robust Distribution Network Reconfiguration." IEEE Transactions on Smart Grid 6, no. 2 (2015): 836–42. http://dx.doi.org/10.1109/tsg.2014.2375160.
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YOUSSEF, Hesham Hanie, Hazlie MOKHLIS, Mohamad Sofian Abu TALIP, Mohammad Al SAMMAN, Munir Azam MUHAMMAD, and Nurulafiqah Nadzirah MANSOR. "Distribution network reconfiguration based on artificial network reconfiguration for variable load profile." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 5 (2020): 3013–35. http://dx.doi.org/10.3906/elk-1912-89.
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Braun, Urs, Axel Schäfer, Henrik Walter, et al. "Dynamic reconfiguration of frontal brain networks during executive cognition in humans." Proceedings of the National Academy of Sciences 112, no. 37 (2015): 11678–83. http://dx.doi.org/10.1073/pnas.1422487112.
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The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the “n-back” task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes “network flexibility,” employs transient and heterogeneous connectivity between frontal systems, which we refer to as “integration.” Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia.
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Zhang, Lianming, Aoyuan Peng, and Jianping Yu. "Reconfiguration and Search of Social Networks." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/391782.
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Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based onk-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency.
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De Baene, Wouter, Martijn J. Jansma, Irena T. Schouwenaars, Geert-Jan M. Rutten, and Margriet M. Sitskoorn. "Task-evoked reconfiguration of the fronto-parietal network is associated with cognitive performance in brain tumor patients." Brain Imaging and Behavior 14, no. 6 (2019): 2351–66. http://dx.doi.org/10.1007/s11682-019-00189-2.
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Abstract In healthy participants, the strength of task-evoked network reconfigurations is associated with cognitive performance across several cognitive domains. It is, however, unclear whether the capacity for network reconfiguration also plays a role in cognitive deficits in brain tumor patients. In the current study, we examined whether the level of reconfiguration of the fronto-parietal (‘FPN’) and default mode network (‘DMN’) during task execution is correlated with cognitive performance in patients with different types of brain tumors. For this purpose, we combined data from a resting state and task-fMRI paradigm in patients with a glioma or meningioma. Cognitive performance was measured using the in-scanner working memory task, as well as an out-of-scanner cognitive flexibility task. Task-evoked changes in functional connectivity strength (defined as the mean of the absolute values of all connections) and in functional connectivity patterns within and between the FPN and DMN did not differ significantly across meningioma and fast (HGG) and slowly growing glioma (LGG) patients. Across these brain tumor patients, a significant and positive correlation was found between the level of task-evoked reconfiguration of the FPN and cognitive performance. This suggests that the capacity for FPN reconfiguration also plays a role in cognitive deficits in brain tumor patients, as was previously found for normal cognitive performance in healthy controls.
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Lin, Jun, and Chen Liang Zhou. "Distribution network reconfiguration optimization based on genetic algorithm and its influence on operation and maintenance management." Molecular & Cellular Biomechanics 21, no. 3 (2024): 744. http://dx.doi.org/10.62617/mcb744.
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As one of the advanced application functions of distribution network automation, distribution network reconfiguration is an important optimization means to ensure the normal operation of the power grid. In order to further improve the power supply quality and operation and maintenance efficiency of the distribution network, this paper proposes a reconfiguration method based on improved genetic algorithm, establishes a network topology reconfiguration computational model, and validates the proposed method for the reconfiguration of the distribution network, and the results show that compared with the reconfiguration model of the distribution network constructed by basic genetic algorithm, the algorithm of this paper shows excellent performance in terms of both the comparison of the node voltages and the evolution of the population. The results show that compared with the basic genetic algorithm constructed distribution network reconfiguration model, this paper's algorithm exhibits excellent performance in both node voltage comparison and population evolution, and is capable of realizing optimal power transmission. Finally, the impact of distribution network reconfiguration on operation and maintenance management is analyzed.
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Li, Xin, Mingyang Li, Moduo Yu, and Qinqin Fan. "Fault Reconfiguration in Distribution Networks Based on Improved Discrete Multimodal Multi-Objective Particle Swarm Optimization Algorithm." Biomimetics 8, no. 5 (2023): 431. http://dx.doi.org/10.3390/biomimetics8050431.
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Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault reconfiguration of the distribution network is often regarded as a single-objective or multi-objective optimization problem, and its multimodality is often ignored in existing studies. Therefore, the obtained solutions may be unsuitable or infeasible when the environment changes. To improve the availability and robustness of the solutions, an improved discrete multimodal multi-objective particle swarm optimization (IDMMPSO) algorithm is proposed to solve the fault reconfiguration problem of the distribution network. To demonstrate the performance of the proposed IDMMPSO algorithm, the IEEE33-bus distribution system is used in the experiment. Moreover, the proposed algorithm is compared with other competitors. Experimental results show that the proposed algorithm can provide different equivalent solutions for decision-makers in solving the fault reconfiguration problem of the distribution network.
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Yang, Qiang, Ergang Zhao, Xin Ma, Lina Xu, Xiuju Du, and Xin Luo. "Optimal reconfiguration of distribution network based on deep fuzzy c-means clustering algorithm." Journal of Physics: Conference Series 2497, no. 1 (2023): 012005. http://dx.doi.org/10.1088/1742-6596/2497/1/012005.
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Abstract This study examines the best distribution network reconfiguration to enhance distribution network safety and reduce active power loss. Distribution networks can be reconfigured using a deep fuzzy C-means (FCM) clustering method with the objective of the least amount of active power loss. By creating a novel framework, the size of the neural network is lowered by modifying the number of neurons in input layer. With the simulations tested on IEEE 33-bus distribution network, the outcomes of traditional approaches, such as the Branch-and-Cut and the switching algorithms, are contrasted with the simulation results. The comparative results clearly show advantages to employing the suggested framework for distribution network reconfiguration, such as a quick turnaround time far quicker than the alternatives. These characteristics demonstrate how well the suggested paradigm may be applied to distribution network real-time reconfiguration.
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Pulyala, Ranjith. "Localized Network Reconfiguration Plan for Wireless Mesh Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 779–805. http://dx.doi.org/10.22214/ijraset.2022.41305.
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Abstract: Wireless mesh networks (WMNs) have emerged as a key technology for next-generation wireless networking. Because of their advantages over other wireless networks, WMNs are undergoing rapid progress and inspiring numerous applications. In multi-hop wireless mesh networks (WMNs) experience frequent link failures caused by channel interference, dynamic obstacles and/or applications’ bandwidth demands. These failures cause severe performance degradation in WMNs or require expensive, manual network management for their real-time recovery. This paper presents an Autonomous network Reconfiguration System (ARS) that enables a multi-radio WMN to autonomously recover from local link failures to preserve network performance. ARS also improves channel efficiency by more than 90% over the other recovery methods. During their lifetime, multi-hop wireless mesh networks (WMNs) experience frequent link failures caused by channel interference, dynamic obstacles, and/or applications’ bandwidth demands. These failures cause severe performance degradation in WMNs or require expensive manual network management for their real-time recovery. By using channel and radio diversities in WMNs, ARS generates necessary changes in local radio and channel assignments in order to recover from failures. Next, based on the thus-generated configuration changes, the system cooperatively reconfigures network settings among local mesh routers. ARS has been evaluated extensively through ns2-based simulation. Our evaluation results show that ARS outperforms existing failure-recovery schemes in improving channel-efficiency by more than 90% and in the ability of meeting the applications’ bandwidth demands by an average of 200%. Keywords: WMN, ARS, AODV, WCETT, ETT, RSVP, DSDV, ETX, ETOPE, BLC
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Shirmohammadi, D. "Service restoration in distribution networks via network reconfiguration." IEEE Transactions on Power Delivery 7, no. 2 (1992): 952–58. http://dx.doi.org/10.1109/61.127104.
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Kashem, M. A., V. Ganapathy, and G. B. Jasmon. "Network reconfiguration for load balancing in distribution networks." IEE Proceedings - Generation, Transmission and Distribution 146, no. 6 (1999): 563. http://dx.doi.org/10.1049/ip-gtd:19990694.
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Reddy, A. V. Sudhakara, M. Damodar Reddy, and M. Satish Kumar Reddy. "Network Reconfiguration of Primary Distribution System Using GWO Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3226. http://dx.doi.org/10.11591/ijece.v7i6.pp3226-3234.
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This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
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Alanazi, Abdulaziz, and Mohana Alanazi. "Artificial Electric Field Algorithm-Pattern Search for Many-Criteria Networks Reconfiguration Considering Power Quality and Energy Not Supplied." Energies 15, no. 14 (2022): 5269. http://dx.doi.org/10.3390/en15145269.
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Considering different objectives and using powerful optimization methods in the distribution networks reconfiguration by accurately achieving the best network configuration can further improve network performance. In this paper, reconfiguration of radial distribution networks is performed to minimize the power loss, voltage sag, voltage unbalance, and energy not supplied (ENS) of customers using a new intelligent artificial electric field algorithm-pattern search (AEFAPS) method based on the many-criteria optimization approach. The voltage sag and voltage unbalance are defined as power quality indices and the ENS is the reliability index. In this study, the pattern search (PS) algorithm enhances the artificial electric field algorithm’s (AEFA) flexibility search both globally and locally. AEFAPS is applied to determine the decision variables as open switches of the networks considering the objective function and operational constraints. The proposed methodology based on AEFAPS is performed on an unbalanced 33-bus IEEE standard network and a real unbalanced 13-bus network. The reconfiguration problem is implemented in single-criterion and many-criteria optimization approaches to evaluate the proposed methodology’s effectiveness using different algorithms. The single-criterion results demonstrated that some power quality indices might be out of range, while all indices are within the permitted range in the many-criteria optimization approach, proving the effectiveness of the proposed many-criteria reconfiguration with logical compromise between different objectives. The results show that AEFAPS identified the network configuration optimally and different objectives are improved considerably compared to the base network. The results confirmed the superior capability of AEFAPS to obtain better objective values and lower values of losses, voltage sag, voltage unbalance, and ENS compared with conventional AEFA, particle swarm optimization (PSO), and grey wolf optimizer (GWO). Moreover, the better performance of AEFAPS is proved in solving the reconfiguration problem compared with previous studies.
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A., V. Sudhakara Reddy, Damodar Reddy M., and Satish Kumar Reddy M. "Network Reconfiguration of Distribution System for Loss Reduction Using GWO Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3226–34. https://doi.org/10.11591/ijece.v7i6.pp3226-3234.
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This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
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Ali, Machrus, Rukslin Rukslin, Hidayatul Nurohmah, Yoga Arie Pambayun, and Achmad Zaini. "Rekonfigurasi Jaringan Distribusi Radial Di Penyulang Purwoasri Berbasis Modified Imperialist Competitive Algorithms (MICA)." Jurnal FORTECH 1, no. 2 (2020): 74–78. http://dx.doi.org/10.32492/fortech.v1i2.227.
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Radial distribution network configuration is difficult to simplify because it is very complex. This network reconfiguration is used to redesign the configuration form of the radial distribution network by opening and closing switches on the distribution network. Purwoasri feeders, Rayon Kertosono, Mojokerto area have very large losses that need to be reconfigured. The resulting power flow will produce a network power loss as a result of the configuration. The reconfiguration process will be repeated until the configuration form that produces the smallest power loss is obtained. The number of feeders and buses on the network will be difficult if done with manual calculations and requires a very long time, so solving the problem must use a computer program. Network reconfiguration using the Matlab 2013a program will analyze its power flow using the Newton Raphson method and using the artificial intelligence method, Modified Imperialist Competitive Algorithms (MICA). With this method, it was obtained before the reconfiguration of the network suffered a loss of 89,724 kWatt after the reconfiguration had a loss of 54.8299 kWatt. The results of reconfiguration can reduce losses of 0.6173 kWatt or 38.95688%.
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Ali, Machrus, Hidayatul Nurohmah, and Dwi Ajiatmo. "Rekonfigurasi Jaringan Distribusi Radial 65 Bus Berbasis Binary Particle Swarm Optimization (BPSO)." Jurnal JEETech 3, no. 1 (2022): 57–61. http://dx.doi.org/10.32492/jeetech.v3i1.3108.
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The configuration of a radial distribution network is difficult to simplify because it is very complex. This network reconfiguration is used to redesign the configuration of the radial distribution network by opening and closing switches on the distribution network. The feeder of Purwoasri, The feeder of Purwoasri, Rayon Kertosono has 65 buses which cause the Mojokerto area to have a very large loss so it needs to be reconfigured.. The resulting power flow will result in network power losses due to configuration. The reconfiguration process will be repeated until a configuration form that produces the smallest power losses is obtained. The number of feeders and buses on the network will be difficult if done manually and takes a very long time, so solving the problem must use a computer program. Network reconfiguration using the Matlab 2013a program will analyze its power flow using the Newton Raphson method and using the Binary Particle Swarm Optimization (BPSO) artificial intelligence method. Before reconfiguration, the network experienced losses of 1169,1374 kWatt after reconfiguration experienced losses of 635,7444 kWatt. The results of the reconfiguration can reduce losses of 635,74440 kWatt or 45,6228 % from the previous loss.
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Li, Ji, Huagang Xiong, Qiao Li, Feng Xiong, and Jiaying Feng. "Run-Time Reconfiguration Strategy and Implementation of Time-Triggered Networks." Electronics 11, no. 9 (2022): 1477. http://dx.doi.org/10.3390/electronics11091477.
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Time-triggered networks are deployed in avionics and astronautics because they provide deterministic and low-latency communications. Remapping of partitions and the applications that reside in them that are executing on the failed core and the resulting re-routing and re-scheduling are conducted when a permanent end-system core failure occurs and local resources are insufficient. We present a network-wide reconfiguration strategy as well as an implementation scheme, and propose an Integer Linear Programming based joint mapping, routing, and scheduling reconfiguration method (JILP) for global reconfiguration. Based on scheduling compatibility, a novel heuristic algorithm (SCA) for mapping and routing is proposed to reduce the reconfiguration time. Experimentally, JILP achieved a higher success rate compared to mapping-then-routing-and-scheduling algorithms. In addition, relative to JILP, SCA/ILP was 50-fold faster and with a minimal impact on reconfiguration success rate. SCA achieved a higher reconfiguration success rate compared to shortest path routing and load-balanced routing. In addition, scheduling compatibility plays a guiding role in ILP-based optimization objectives and ‘reconfigurable depth’, which is a metric proposed in this paper for the determination of the reconfiguration potential of a TT network.
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Popli, Abhinish. "Advanced Autonomous Network Reconfiguration System." IOSR Journal of Computer Engineering 01, no. 01 (2016): 01–06. http://dx.doi.org/10.9790/0661-15010010101-06.
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Rajesh Kumar, V., and V. Sai Kiran. "Optimal reconfiguration of distribution network." International Journal of Engineering & Technology 7, no. 4 (2018): 2614. http://dx.doi.org/10.14419/ijet.v7i2.18.11694.
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This paper proposes a simple method for solving the distribution system reconfiguration to improve the voltage profile and reduce the losses. In this paper power flow studies are used in matrix form of distribution system with radial, weakly meshed and reconfigured forms. The execution of power flow will specify the bus voltages and branch currents. These parameters are used to analyse the system performance and especially branch currents information is used to convert system radial form to weakly meshed and reconfiguration forms by closing the tie-switches and opening the sectionalizing switches. The same information can be used in large networks to show the improvement in voltage profile and reduction in electrical losses.
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Xin, Ning, Laijun Chen, Linrui Ma, and Yang Si. "A Rolling Horizon Optimization Framework for Resilient Restoration of Active Distribution Systems." Energies 15, no. 9 (2022): 3096. http://dx.doi.org/10.3390/en15093096.
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Network reconfiguration is an effective way to avoid severe, large-scale power outages and to improve the resilience of active distribution networks (ADNs). Furthermore, the rapid development of distributed energy resources (DERs) provides new perspectives for network reconfiguration. In this paper, the effect of network reconfiguration and DER collaboration on ADN’s resilient restoration are studied. The applications of DERs are fully explored. In order to achieve a better resilient performance, a detailed multiperiod model considering both reconfiguration and multiple DERs is established. Some linearization techniques are used to simplify the proposed model. Then, we build a rolling horizon optimization framework to solve the model. The framework eliminates the adverse effect of prediction errors and speeds up the calculation. By introducing predictions into strategy determination, the framework achieves a better restoration effect than the traditional greedy method. The proposed framework is tested on a 33-bus system. The simulations verify the efficiency of our work.
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Akbar Firdaus, Aji, Ontoseno Penangsang, Adi Soeprijanto, and Dimas Fajar U.P. "Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index." Bulletin of Electrical Engineering and Informatics 7, no. 4 (2018): 514–21. http://dx.doi.org/10.11591/eei.v7i4.821.
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Power losses and voltage drop are existing problems in radial distribution networks. This power losses and voltage drop affect the voltage stability level. Reconfiguring the network is a form of approach to improve the quality of electrical power. The network reconfiguration aims to minimize power losses and voltage drop as well as decreasing the Voltage Stability Index (VSI). In this research, network reconfiguration uses binary particle swarm optimization algorithm and Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method to analyze the radial system power flow. This scheme was tested on the 33-bus IEEE radial distribution system 12.66 kV. The simulation results show that before reconfiguration, the active power loss is 202.7126 kW and the VSI is 0.20012. After reconfiguration, the active power loss and VSI decreased to 139.5697 kW and 0.14662, respectively. It has decreased the power loss for 31.3136% significantly while the VSI value is closer to zero.
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Firdaus, Aji Akbar, Ontoseno Penangsang, Adi Soeprijanto, and Dimas Fajar U. P. "Distribution Network Reconfiguration Using Binary Particle Swarm Optimization to Minimize Losses and Decrease Voltage Stability Index." Bulletin of Electrical Engineering and Informatics 7, no. 4 (2018): 514–21. https://doi.org/10.11591/eei.v7i4.821.
Full textAbstract:
Power losses and voltage drop are existing problems in radial distribution networks. This power losses and voltage drop affect the voltage stability level. Reconfiguring the network is a form of approach to improve the quality of electrical power. The network reconfiguration aims to minimize power losses and voltage drop as well as decreasing the Voltage Stability Index (VSI). In this research, network reconfiguration uses binary particle swarm optimization algorithm and Bus Injection to Branch Current-Branch Current to Bus Voltage (BIBC-BCBV) method to analyze the radial system power flow. This scheme was tested on the 33-bus IEEE radial distribution system 12.66 kV. The simulation results show that before reconfiguration, the active power loss is 202.7126 kW and the VSI is 0.20012. After reconfiguration, the active power loss and VSI decreased to 139.5697 kW and 0.14662, respectively. It has decreased the power loss for 31.3136% significantly while the VSI value is closer to zero.
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Li, Chuan Jian, Nan Hua Yu, Hui Chen, and Wen Jie Zheng. "Overview on Distribution Network Reconfiguration Optimization Algorithm." Advanced Materials Research 516-517 (May 2012): 1400–1407. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.1400.
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With the increasing demands are beyond the capacity of the power generation and the progressive demands for higher power quality, it requires for further study on distribution system reconfiguration. This paper describes the distribution network reconfiguration algorithms in detail. The advantages and disadvantages of the various algorithms and improvement are also analyzed in this paper. At the end of the paper, the development directions of distribution network reconfiguration are discussed.
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Machrus Ali and Miftachul Ulum. "Penataan Kembali Jaringan Distribusi Radial Menggunakan Modified Firefly Algorithm (MFA) Pada Penyulang Jogorogo Ngawi." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 8, no. 2 (2017): 86–91. http://dx.doi.org/10.48056/jintake.v8i2.30.
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The configuration of radial distribution networks is very diverse and difficult to simplify. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches found on the distribution network. Reconfiguration of the distribution network is needed to reduce power losses and improve system reliability. The number of feeders and buses on the network requires a very long time if calculated manually. Because of that, it is necessary to solve problems using artificial intelligence or AI. Firefly Algorithms (FA) are widely used in research to solve optimization problems. Modified Firefly Algorithms (MFA) is an FA modification designed to solve optimization problems in a discrete combination. MFA can find the best network reconfiguration so that it can reduce losses and improve the voltage at the end point of this method can later use other artificial intelligence or can be applied to other feeders, so as to reduce electrical energy losses.
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Ali, Machrus, and Rukslin Rukslin. "Penataan Kembali Jaringan Distribusi Radial Menggunakan Modified Firefly Algorithm (MFA) Pada Penyulang Jogorogo Ngawi." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 8, no. 2 (2017): 86–91. http://dx.doi.org/10.32492/jintake.v8i2.767.
Full textAbstract:
The configuration of radial distribution networks is very diverse and difficult to simplify. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches found on the distribution network. Reconfiguration of the distribution network is needed to reduce power losses and improve system reliability. The number of feeders and buses on the network requires a very long time if calculated manually. Because of that, it is necessary to solve problems using artificial intelligence or AI. Firefly Algorithms (FA) are widely used in research to solve optimization problems. Modified Firefly Algorithms (MFA) is an FA modification designed to solve optimization problems in a discrete combination. MFA can find the best network reconfiguration so that it can reduce losses and improve the voltage at the end point of this method can later use other artificial intelligence or can be applied to other feeders, so as to reduce electrical energy losses.
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Khan, Muhammad Omer, Abdul Wadood, Muhammad Irfan Abid, Tahir Khurshaid, and Sang Bong Rhee. "Minimization of Network Power Losses in the AC-DC Hybrid Distribution Network through Network Reconfiguration Using Soft Open Point." Electronics 10, no. 3 (2021): 326. http://dx.doi.org/10.3390/electronics10030326.
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The Alternating Current-Direct Current (AC-DC) hybrid distribution network has received attention in recent years. Due to advancement in technologies such as the integration of renewable energy resources of DC–type output and usage of DC loads in the distribution network, the modern distribution system can meet the increasing energy demand with improved efficiency. In this paper, a new AC-DC hybrid distribution network architecture is analyzed that considers distributed energy resources (DER) in the network. A network reconfiguration scheme is proposed that uses the AC soft open point (AC-SOP) and the DC soft open point (DC-SOP) along with an SOP selection algorithm for minimizing the network power losses. Subsequently, the real-time data for DER and load/demand variation are considered for a day-a-head scenario for the verification of the effectiveness of the network reconfiguration scheme. The results show that the proposed network reconfiguration scheme using AC-SOP and DC-SOP can successfully minimize the network power losses by modifying the network configuration. Finally, the effectiveness of the proposed scheme in minimizing the network power losses by the upgraded network configuration is verified by constructing an AC-DC hybrid distribution network by combining two IEEE 33-bus distribution networks.
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Alanazi, Mohana, Abdulmajeed Alanazi, Kareem M. AboRas, and Yazeed Yasin Ghadi. "Multiobjective and Coordinated Reconfiguration and Allocation of Photovoltaic Energy Resources in Distribution Networks Using Improved Clouded Leopard Optimization Algorithm." International Journal of Energy Research 2024 (January 17, 2024): 1–19. http://dx.doi.org/10.1155/2024/7792658.
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In this paper, a multiobjective framework for simultaneous reconfiguration and allocation of photovoltaic (PV) energy resources in radial distribution networks is performed for minimizing the power losses, lowering network loading factor, and reducing cost of energy resources as well as increasing the voltage stability index. A novel algorithm called the improved clouded leopard algorithm is used to find the set of optimum decision factors in the combined execution of restructuring and also PV resource distribution. The clouded leopard optimization (CLO) algorithm, which is widely used, takes its cues from the sleeping and foraging habits of the animal, and the improved CLO (ICLO) is formed using adaptive inertia weight to overcome premature convergence. In five cases of base distribution network and different contribution of reconfiguration and PV allocation, single- and multiobjective approach has been applied on 33- and 69-bus distribution networks. According to the findings, the best case includes simultaneous reconfiguration and PV allocation in the radial networks based on the multiobjective approach unlike the single-objective method which obtained the highest network performance with the best compromise between different goals. Also, in the best lowest losses, the decrease in the network loading and cost of energy resources and the better voltage profile and stability are obtained satisfying the constraints. The losses, network loading, voltage profile, and voltage stability are improved by 60.40%, 37.89%, 6.17%, and 27.5% for 33-bus network and also enhanced by 71.77%, 41.23%, 4.76%, and 20.48% for 69-bus network, respectively. Also, the results showed that network reconfiguration only has the weakest performance among other cases based on reconfiguration or photovoltaic sources. Moreover, the superior capability of the ICLO in addressing the aim of the study is proved in comparison with the traditional CLO and well-known particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) to obtain the better objective value and statistical criteria to solve the best case.
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Hasibuan, Arnawan, Kartika Kartika, and Sahal Mahfudz. "Reconfiguration of 20 kV Substance Connection Network LG-04 Lhokseumawe City Using Digsilent Power Factory 15.1 Software Simulation." International Journal of Advances in Data and Information Systems 3, no. 1 (2022): 19–28. http://dx.doi.org/10.25008/ijadis.v3i1.1232.
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In this final project, we describe the study of reconfiguring the distribution network at the LG-04 feeder at the Lancang Garam substation, Lhokseumawe, to improve the performance of the distribution system. The focus of the parameters considered includes continuity, voltage, and power loss. The reconfiguration is to change the initial network topology which is in the form of radial into a loop. After reconfiguration, the calculation of power flow, voltage drop, power loss, and short circuit calculation is also carried out which becomes a reference for whether or not the quality of the network is good. The results of the simulation of the existing conditions show that continuity is not good, after reconfiguration and fault testing, the network does not experience blackouts like the network before reconfiguration. The power loss generated in the network after reconfiguration is 0.507 kW or a decrease of ±84% from the previous form of the network.
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Manivannan Mathivanan Muthu, Jeevabarathy. "Routing using Autonomous Network Reconfiguration System with Bandwidth Guarantees." International Journal of Scientific Engineering and Research 2, no. 1 (2014): 34–37. https://doi.org/10.70729/j201397.
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Devaux, Ludovic, Sana Ben Sassi, Sebastien Pillement, Daniel Chillet, and Didier Demigny. "Flexible Interconnection Network for Dynamically and Partially Reconfigurable Architectures." International Journal of Reconfigurable Computing 2010 (2010): 1–15. http://dx.doi.org/10.1155/2010/390545.
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The dynamic and partial reconfiguration of FPGAs enables the dynamic placement in reconfigurable zones of the tasks that describe an application. However, the dynamic management of the tasks impacts the communications since tasks are not present in the FPGA during all computation time. So, the task manager should ensure the allocation of each new task and their interconnection which is performed by a flexible interconnection network. In this article, various communication architectures, in particular interconnection networks, are studied. Each architecture is evaluated with respect to its suitability for the paradigm of the dynamic and partial reconfiguration in FPGA implementations. This study leads us to propose the DRAFT network that supports the communication constraints into the context of dynamic reconfiguration. We also present DRAGOON, the automatic generator of networks, which allows to implement and to simulate the DRAFT topology. Finally, DRAFT and the two most popular Networks-on-Chip are implemented in several configurations using DRAGOON, and compared considering real implementation results.
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Muhammad Ridzuan, Mohd, and Sasa Djokic. "Energy Regulator Supply Restoration Time." Energies 12, no. 6 (2019): 1051. http://dx.doi.org/10.3390/en12061051.
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In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconfiguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconfiguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconfiguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reflected the applicability of reconfiguration and the replace/repair time of network component. Theoretically, the network reconfiguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply.
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R. N. Sandhiya. "Adaptive Load-Balanced Clustering for Enhanced Energy Efficiency and Fault Tolerance in Wireless Sensor Networks." Journal of Information Systems Engineering and Management 10, no. 35s (2025): 1177–91. https://doi.org/10.52783/jisem.v10i35s.6288.
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Wireless Sensor Networks (WSNs) are critical in distributed monitoring systems, where optimal performance relies on efficient energy usage, balanced data handling, and fault resilience. Traditional clustering protocols, such as Energy-Aware Hybrid Clustering (EAHC), primarily focus on energy metrics but often neglect real-time load balancing and adaptive reconfiguration. This leads to node failures, uneven energy depletion, and increased latency. Clustering methods lacking dynamic adaptation to node load and failure conditions often face performance degradation due to hotspot formation, unbalanced load, and delayed reconfiguration. These issues negatively affect network lifetime, throughput, and data latency, particularly in large-scale and heterogeneous WSNs. The proposed Adaptive Load-Balanced Clustering (ALBC) model addresses these limitations by dynamically forming clusters based on real-time metrics such as node load, energy levels, data rates, and fault tolerance. A mathematical framework involving energy and data rate constraints, load variance minimization, and reconfiguration cost is developed. The cluster head (CH) selection process favors nodes with optimal energy-to-load ratios while ensuring connectivity and minimal latency. The model is validated using MATLAB simulations against four existing models: EAHC, LEACH, HEED, and EEHC. Simulation results show that ALBC significantly reduces energy consumption and latency while enhancing load balance and fault tolerance. It achieves up to 22% lower load variance, 18% higher throughput, and 30% fewer reconfigurations compared to EAHC. The model adapts seamlessly to node failures, ensuring uninterrupted data flow and prolonged network lifespan.
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He, Yuwei, Yanjun Li, Jian Liu, et al. "A Two-Stage Fault Reconfiguration Strategy for Distribution Networks with High Penetration of Distributed Generators." Electronics 14, no. 9 (2025): 1872. https://doi.org/10.3390/electronics14091872.
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In distribution networks with high penetration of distributed generators (DGs), traditional fault reconfiguration strategies often fail to achieve maximum load recovery and encounter operational stability challenges. This paper proposes a novel two-stage fault reconfiguration strategy that addresses both the fault ride-through capability and output uncertainty of DGs. The first stage introduces a rapid power restoration reconfiguration model that integrates network reconfiguration with fault ride-through, enabling DGs to provide power support to the distribution network during faults, thereby significantly improving the recovery rate of lost loads. An AdaBoost-enhanced decision tree algorithm is utilized to accelerate the computational process. The second stage proposes a post-recovery optimal reconfiguration model that uses fuzzy mathematics theory and the transformation of chance constraints to quantify the uncertainty of both generation and load, thereby improving the system’s static voltage stability index. Case studies using the IEEE 69-bus system and a real-world distribution network validate the effectiveness of the proposed strategy. This two-stage strategy facilitates short-term rapid load power restoration and enhances long-term operational stability, improving both the resilience and reliability of distribution networks with high DG penetration. The findings of this research contribute to enhancing the fault tolerance and operational efficiency of modern power systems, which is essential for integrating higher levels of renewable energy.
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Li, Junjun, Hang Zhao, and Bowei Xu. "Optimization of Container Shipping Network Reconfiguration under RCEP." Journal of Marine Science and Engineering 10, no. 7 (2022): 873. http://dx.doi.org/10.3390/jmse10070873.
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Due to its advantages of large transportation volume and low transportation cost, container shipping has become an important transportation mode in current international trade. The recovery of the shipping industry in 2021 and the signing of RCEP make the reconfiguration and optimization of the container shipping network a very important task at present. The network service capability is an important factor affecting the container shipping network. Based on the complex network theory, the coefficients of port location, the importance of distance and route among ports are taken as the service capacity reconfiguration coefficients of the global container shipping network. A max-min mixed integer model is established for global container shipping network reconfiguration. A sort of communication-reducing conjugate gradient method based on Krylov Subspace (CR-CG-KS) is proposed to reduce the reconfiguration computation. The results show that the global container shipping network does not need large-scale reconfiguration but requires small-scale changes to optimize the network feature vector centrality and make the network more balanced and stable. This study is beneficial for business managers to proactively respond to the future development of the shipping network and improve the operational efficiency of the global container shipping network.
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Rong, Hai Na, and Yan Hui Qin. "Distribution Network Reconfiguration Using an Approximate Optimization Approach." Applied Mechanics and Materials 303-306 (February 2013): 1276–79. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1276.
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Power network reconfiguration is an important process in the improvement of operating conditions of a power system and in planning studies, service restoration and distribution automation when remote-controlled switches are employed. This paper presents the use of a quantum-inspired evolutionary algorithm to solve the distribution network reconfiguration problem. The quantum- inspired evolutionary algorithm is the combination product of quantum computing and evolutionary computation and is suitable for a class of integer programming problems such as the distribution network reconfiguration problem. After the analysis and formulation of the distribution network reconfiguration problem, the effectiveness and feasibility of the introduced method is verified by a large number of experiments.
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Hasibuan, Arnawan, Asran Asran, Fery Adriansyah, Baharuddin Ismail, Widyana Verawaty Siregar, and Muhammad Sayuti. "Planning to Reconfigure the 150 kV Transmission Network at PT. PLN (Persero) Tualang Cut Network and Substation." Journal of Renewable Energy, Electrical, and Computer Engineering 3, no. 2 (2023): 61. http://dx.doi.org/10.29103/jreece.v3i2.11740.
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System condition at PT. PLN (Persero) Tualang Cut Substation where the reliability of the 150 kV system depends on the Langsa Substation, which we will reconfigure on the Lgs-P.B and Lgs-T.C channels to P.B-T. C and T.C-Lgs. The planning for the reconfiguration of the 150 kv transmission network of the Tualang Cut Substation was carried out by estimating the distance of the transmission line and the constraints that occurred. The condition of the electric power system that is currently operating at PT. PLN (Persero) Tualang Cut Substation is considered less efficient in receiving power from the 150 kV SUTT interconnection. The SUTT in Tualang Cut currently receives electrical power from the Langsa Substation with a Losses value on lines 1 and 2 of 771,4142474 kW and experiences a Voltage Drop on lines 1 and 2 of 4,38 kV. After we reconfigured the channel, from the results of the reconfiguration there was a decrease in Losses on channels 1 and 2 by 311,298581 kW which before the reconfiguration of power losses on the line amounted to 771,4142474 kW and after being reconfigured to 460,1156668 kW there was a decrease in Voltage Drop on channels 1 and 2 by 1,44 kV, before the reconfiguration of the voltage drop on the line of 4,38 kV and after the reconfiguration to 2,94 kV, the reconfiguration carried out is the best solution to reduce losses and voltage drops.
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Gomes, Thiago Eliandro de Oliveira, Vinícius Jacques Garcia, and André Ross Borniatti. "Analysis of Clustering Algorithms Applied to Distribution Network Reconfiguration." Revista de Gestão Social e Ambiental 18, no. 11 (2024): e09905. http://dx.doi.org/10.24857/rgsa.v18n11-166.
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Objective: The study aims to speed up the reconfiguration process of electrical distribution networks. Using graph theory, it explores methods for the network's topological decomposition and searches for lower-cost paths to optimize the distribution network configuration. Related Studies: The research is based on graph theory, which allows modeling the connections of an electrical network as an interconnected graph. Important references include clustering techniques and algorithms for finding minimum paths in graphs, applying methods such as Malgrange, K-means, DBSCAN, SOM, and classical graph search algorithms like Dijkstra and Floyd-Warshall. Method: The methodology includes applying different algorithms for topological reduction and path search, analyzing their performances. The IEEE 37-bus system was used as a test model. The studied algorithms included Malgrange, K-means, Ward (hierarchical), DBSCAN, SOM, and minimum path algorithms. Results and Discussion: The results indicated that the Malgrange method brought a significant reduction in the search space, with a decrease of approximately 83.5% in possible switch combinations. However, in larger and more complex networks, increasing the number of clusters could contribute to even greater efficiency. The research concluded that minimum-path search algorithms produced similar results. Research Implications: The research offers a methodology that can be applied in power distribution networks to reduce computational time and improve the efficiency of reconfiguration processes. Practical implications include direct use by power distribution companies looking to optimize their networks. Theoretically, it reinforces the use of graph theory in network optimization problems. Originality/Value: The originality lies in combining graph techniques for decomposition and minimum-path search in electrical networks, with significant optimization results. The research contributes insights for future implementations of reconfiguration in complex networks, highlighting the value of graph analysis as a robust approach to electrical distribution problems.
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Hota, Ambika Prasad, and Sivkumar Mishra. "Loss allocation in distribution networks with distributed generators undergoing network reconfiguration." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3375. http://dx.doi.org/10.11591/ijece.v10i4.pp3375-3383.
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In this paper, a branch exchange based heuristic network reconfiguration method is proposed for obtaining an optimal network in a deregulated power system. A unique bus identification scheme is employed which makes the load flow and loss calculation faster due to its reduced search time under varying network topological environment. The proposed power loss allocation technique eliminates the effect of cross-term analytically from the loss formulation without any assumptions and approximations. The effectiveness of the proposed reconfiguration and loss allocation methods are investigated by comparing the results obtained by the present approach with that of the existing “Quadratic method” using a 33-bus radial distribution system with/without DGs.
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Su, Yuze, Xiangru Meng, Qiaoyan Kang, and Xiaoyang Han. "Dynamic Virtual Network Reconfiguration Method for Hybrid Multiple Failures Based on Weighted Relative Entropy." Entropy 20, no. 9 (2018): 711. http://dx.doi.org/10.3390/e20090711.
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Network virtualization can offer more flexibility and better manageability for next generation Internet. With the increasing deployments of virtual networks in military and commercial networks, a major challenge is to ensure virtual network survivability against hybrid multiple failures. In this paper, we study the problem of recovering virtual networks affected by hybrid multiple failures in substrate networks and provide an integer linear programming formulation to solve it. We propose a heuristic algorithm to tackle the complexity of the integer linear programming formulation, which includes a faulty virtual network reconfiguration ranking method based on weighted relative entropy, a hybrid multiple failures ranking algorithm, and a virtual node migration method based on weighted relative entropy. In the faulty virtual network reconfiguration ranking method based on weighted relative entropy and virtual node migration method based on weighted relative entropy, multiple ranking indicators are combined in a suitable way based on weighted relative entropy. In the hybrid multiple failures ranking algorithm, the virtual node and its connective virtual links are re-embedded, firstly. Evaluation results show that our heuristic method not only has the best acceptance ratio and normal operation ratio, but also achieves the highest long-term average revenue to cost ratio compared with other virtual network reconfiguration methods.
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Singh, Seema, and T. V. Rama Murthy. "Neural Network-Based Sensor Fault Accommodation in Flight Control System." Journal of Intelligent Systems 22, no. 3 (2013): 317–33. http://dx.doi.org/10.1515/jisys-2013-0032.
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AbstractThis article deals with detection and accommodation of sensor faults in longitudinal dynamics of an F8 aircraft model. Both the detection of the fault and reconfiguration of the failed sensor are done with the help of neural network-based models. Detection of a sensor fault is done with the help of knowledge-based neural network fault detection (KBNNFD). Apart from KBNNFD, another neural network model is developed in this article for the reconfiguration of the failed sensor. A model-based approach of the neural network (MBNN) is developed, which uses the radial basis function of the neural network. MBNN successfully does the task of providing analytical redundancy for the aircraft sensor. In this work, both detection and reconfiguration of a fault is done using neural networks. Hence, the control system becomes robust for handling sensor failures near steady state and reconfiguration is also faster. A generalized regression neural network (GRNN), which is a type of radial basis network, is used for MBNN, which gives very efficient results for function approximation. An F8 aircraft model and C-Star controller, which improves its handling quality, are used for validation of the method involved. Models of F8 aircraft, C-Star controller, KBNNFD, and MBNN were developed using MATLAB/Simulink. Successful implementation and simulation results are shown and discussed using Simulink.
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Guo, Shuang, Liang Liu, Tengxiang Jing, and Huan Liu. "SFC active reconfiguration based on user mobility and resource demand prediction in dynamic IoT-MEC networks." PLOS ONE 19, no. 8 (2024): e0306777. http://dx.doi.org/10.1371/journal.pone.0306777.
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To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge. In this paper, we study the SFC Reconfiguration Strategy (SFC-RS) based on user mobility and resource demand prediction in IoT MEC networks, aiming to minimize the end-to-end delay and reconfiguration cost of SFCs. First, we model SFC-RS as Integer Linear Programming (ILP). Then, a user trajectory prediction model based on codec movement with attention mechanism and a VNF resource demand prediction model based on the Long Short-Term Memory (LSTM) network are designed to accurately predict user trajectories and node computational and storage resources, respectively. Based on the prediction results, a Prediction-based SFV Active Reconfiguration (PSAR) algorithm is proposed to achieve seamless SFC migration and routing update before the user experience quality degrades, ensuring network consistency and high quality service. Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.
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Ridha, Djamel Mohammedi, Zine Rabie, Mosbah Mustafa, and Arif Salem. "Optimum Network Reconfiguration using Grey Wolf Optimizer." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2428–35. https://doi.org/10.12928/TELKOMNIKA.v16i5.10271.
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Distribution system Reconfiguration is the process of changing the topology of the distribution network by opening and closing switches to satisfy a specific objective. It is a complex, combinatorial optimization problem involving a nonlinear objective function and constraints. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic search algorithm inspired by the leadership hierarchy and hunting strategy of grey wolves in nature. The objective of this paper is to determine an optimal network reconfiguration that presents the minimum power losses, considering network constraints, and using GWO algorithm. The proposed algorithm was tested using some standard networks (33 bus, 69 bus, 84 bus and 118 bus), and the obtained results reveal the efficiency and effectiveness of the proposed approach.