Flexible Network Research Articles

DC line fault discrimination based on maximum information coefficient

In DC networks, conventional line differential protection has the disadvantages of low reliability, weak resistance to transition resistance, and vulnerable to harmonic interference. In this paper, a fault identification method based on maximal information coefficient (MIC) is proposed for non-high resistance faults in the DC side of a flexible DC distribution network containing a modular multilevel converter (MMC) with single pole grounding DC protection inside and outside segments. The failure prediction is performed based on the reciprocal information at two sides of the line current. At the same time, for single-pole grounded high-resistance faults, the MIC is compared using the zero moduli of the current at two sides of the line for the recognition of high-resistance faults. The MMC-based flexible DC transmission network model is built and validated in the PSCAD/EMTDC platform. The results show that the method can identify faults protection inside and outside segments on the DC line area quickly and reliably. It also has the ability to resist harmonic interference and has an endurance of resistance for single-pole faults on the DC side; meanwhile, for high-resistance faults, it can also be detected accurately.

Research on optimal scheduling method of active distribution network considering multi-type flexible resources

With the increase in the utilization rate of renewable energy, the volatility and uncertainty of active distribution networks have become more significant. Comprehensive consideration of the coordination and optimization of multiple resources can improve the flexibility of the system and promote energy consumption. Effective substitution Quantitative modeling is carried out considering a large number of flexible resources based on the flexibility of nodes and networks, combined with the physical characteristics of node flexible resources. Through the analysis of the implementation principle, control mode, and operation characteristics of the soft open point of network flexibility resource, the operation model of the soft open point with combined energy storage is established. On this basis, there are two elasticity evaluation indicators: elasticity adequacy and transmission adequacy. And we incorporate flexible benefits into optimization goals. Multiple optimization scheduling models have been created, including economy node flexibility, and network flexibility. Finally, multiple target particle swarm optimization algorithms will use the proposed scheduling model, combined with trend calculation and validation, using an improved IEEE33 node system.

Optimal Configuration for Soft Open Point Based on Scenario Generation of Wind Power and Photovoltaic Output

Soft Open Point (SOP) is a new type of fully controlled power electronic device. The use of SOPs can greatly improve the efficiency, flexibility, and controllability of distribution networks. However, considering that SOPs are expensive and have high maintenance costs, the installation capacity and location of SOPs need to be optimized. In this paper, we propose an SOP optimization configuration method considering the stochasticity and correlation of wind and PV power generation. First, considering the uncertainty and negative correlation of wind and PV power output, the probability density distribution function is generated using Copula theory and historical data. Then, the optimal location of the SOPs is determined by a cluster discovery algorithm and the Power Loss Sensitivity Index (PLSI). Finally, the capacity of the SOP is determined by minimizing the integrated cost of the network over a year, and the capacity of the SOP is solved by the conic curve programming method. The superiority of the proposed method is verified in this study at the IEEE-33 node test system.

Improving the Flexibility of the Distribution Network Based on the Robust Chance Constraint Method

While the large-scale access to distributed new energy brings economic and environmental benefits, its inherent uncertainty characteristics also trigger problems such as voltage overruns and fluctuations. In order to effectively solve the adverse effects of large-scale grid connection of distributed new energy, this paper proposes the optimization method of regional autonomy capacity based on the robust chance constraint model. First, the regional autonomy capacity measurement method based on distribution robust optimization is studied, and the regional autonomy robust chance constraint model containing distributed energy is constructed. Second, the active distribution network scheduling model is studied to achieve the optimization of regional self-energy capacity. Finally, simulation examples show that the proposed approach can fully utilize the flexible resources within the distribution network and effectively improve the autonomy of the distribution network park.

Reinforcement Learning-Based Particle Swarm Optimization for End-to-End Traffic Scheduling in TSN-5G Networks

With the rapid development of the Industrial Internet of Things (IIoT), massive IIoT devices connect to industrial networks via wired and wireless. Furthermore, industrial networks pose new requirements on communications, such as strict latency boundaries, ultra-reliable transmission, and so on. To this end, time-sensitive networking (TSN) embedded fifth-generation (5G) wireless communication technology (i.e., TSN-5G networks), is considered the most promising solution to address these challenges. TSN can provide deterministic end-to-end latency and reliability for real-time applications in wired networks. 5G supports ultra-reliable and low-latency communications (uRLLC), providing increased flexibility and inherent mobility support in the wireless network. Thus, the integration of TSN and 5G provides numerous benefits, including increased flexibility, lower commissioning costs, and seamless interoperability of various devices, regardless of whether they use a wired or wireless interface. Nonetheless, the potential barriers between the TSN and 5G systems, such as clock synchronization and end-to-end traffic scheduling, are inevitable. Time synchronization has been studied in many works, so this paper focuses on the end-to-end traffic scheduling problem in TSN-5G networks. We propose a novel integrated TSN and 5G industrial network architecture, where the 5G system acts as a logical TSN-capable bridge. Based on this network architecture, we design a Double Q-learning based hierarchical particle swarm optimization algorithm (DQHPSO) to search for the optimal scheduling solution. The DQHPSO algorithm adopts a level-based population structure and introduces Double Q-learning to adjust the number of levels in the population, which evades the local optimum to further improve the search efficiency. Extensive simulations demonstrate that the DQHPSO algorithm can increase the scheduling success ratio of time-triggered flows compared to other algorithms.

A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.

Forecasting global spread of invasive pests and pathogens through international trade

AbstractNon‐native plant pests and pathogens threaten biodiversity, ecosystem function, food security, and economic livelihoods. As new invasive populations establish, often as an unintended consequence of international trade, they can become additional sources of introductions, accelerating global spread through bridgehead effects. While the study of non‐native pest spread has used computational models to provide insights into drivers and dynamics of biological invasions and inform management, efforts have focused on local or regional scales and are challenged by complex transmission networks arising from bridgehead population establishment. This paper presents a flexible spatiotemporal stochastic network model called PoPS (Pest or Pathogen Spread) Global that couples international trade networks with core drivers of biological invasions—climate suitability, host availability, and propagule pressure—quantified through open, globally available databases to forecast the spread of non‐native plant pests. The modular design of the framework makes it adaptable for various pests capable of dispersing via human‐mediated pathways, supports proactive responses to emerging pests when limited data are available, and enables forecasts at different spatial and temporal resolutions. We demonstrate the framework using a case study of the invasive planthopper spotted lanternfly (Lycorma delicatula). The model was calibrated with historical, known spotted lanternfly introductions to identify potential bridgehead populations that may contribute to global spread. This global view of phytosanitary pandemics provides crucial information for anticipating biological invasions, quantifying transport pathways risk levels, and allocating resources to safeguard plant health, agriculture, and natural resources.

Control and Fault Protection Strategy in DC Distribution Network

This article provides a detailed analysis of the dynamic characteristics of DC pole-to-pole faults in modular multilevel converters and proposes a protection and control strategy based on the current rate of change. By detecting the current rate of change, a resistance current limiter is put into operation to achieve DC bipolar short circuit non-blocking fault crossing. The fault current is cut off by the DC circuit breaker. This method is an active current limiting control method that adds current limiting devices before fault current identification, leading to fault identification. Finally, a single-ended flexible DC distribution network model was built by using a real-time simulation system. The simulation results verified the effectiveness and accuracy of the protection and control strategy in the case of pole-to-pole faults.

Flow Table Overflow Attacks in Software Defined Networks: A Survey

<p>While Software-Defined Networks (SDNs) have separated control and data planes and completely decouple the flow control from the data forwarding to enable network flexibility, programmability, and innovation, they also raise serious security concerns in each plane and the interfaces between the two planes. This paper, instead of studying the security issues in the SDN control plane as many literatures have done in current research, focuses on the security issues in the SDN data plane, aiming at the state of the art mechanims to identify, detect, and mitigate them. Specifically, this paper reviews the typical models, detections, and mitigations of SDN flow table overflow attacks. After reviewing the various vulnerabilities in SDNs, this paper categorizes the flow table overflow attacks into saturation, low-rate table exhaustion, and slow saturation attacks, and summarizes the attack models, detections, and mitigations of each category. It reviews the typical attacks that can overflow the flow tables and provides the main challenges and open issues for the future research.</p> <p> </p>

Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks

Existing space application networks in space stations are mainly fiber-optic cable-based networks due to their low size, weight, and power (SWaP) values. While fiber networks in space stations offer data transmission at high speeds with minimal signal loss, their major disadvantage is the lack of flexibility and mobility when new and unplanned space scientific equipment is added to the network. To enhance the flexibility of space networks while increasing their throughput, this paper introduces the hybrid space network (HSN), a new space network architecture that incorporates an optical wireless link, to meet the ever-increasing demands for larger bandwidth and higher mobile access capabilities in space scientific experiments. To best utilize the HSN’s system performance, we propose a multi-priority-based network scheduling scheme, which can dynamically adapt to the requirements of mass tasks and select the best transmission procedure. Through simulations, we find that by adding optical wireless communication (OWC) links to the state-of-the-art deterministic FC-AE-1553 space network, the HSN’s bandwidth can be increased by 20 times with an average latency reduction of 87.3%. We believe that the proposed HSN’s architecture may ultimately shape the future of space stations’ wireless connectivity, and in the meantime, innovate many advanced space applications with larger data rates and mobility requirements.

Fulfillment flexibility strategy for dual-channel retail networks

Flexibility design has been widely adopted in practice as a competitive strategy to respond effectively to uncertainties. In this article, we analyze the fulfillment flexibility for dual-channel retail networks, in which the firms should fulfill both online demands from retailing platforms and in-store offline demands. In particular, by setting the order of fulfillment, we find that a dual-channel retail network can be equivalently transformed to an online retail network with stochastic inventory and demand. By implementing copositive programming, we obtain an asymptotic robust lower bound for the ratio of expected sales to fully flexible expected sales under a K-chain design. This bound only depends on the partial moment information and support set of demands, rather than the complete demand distribution information. Interestingly, we derive the optimality of a K-chain in symmetric balanced networks and the performance of the K-chain under different distributions is robust. In addition, numerical experiments are conducted to further deliver some insights for practitioners. The uncertainties of in-store demand or inventory will reduce the expected sales while both fulfillment flexibility and safety inventory can be used to enhance the performance of a retail system. Finally, we find that the correlation coefficient between in-store demand and online demand will affect the decision-making significantly.

Research on the Implementation Path of Tourism Contributing to the Development of Rural Revitalization Combined with Elastic Regression Network Algorithm

Abstract This paper analyzes the power mechanism of tourism to help rural revitalization by elaborating the core content of rural tourism and rural revitalization. It also combines the advantages of the Lasso regression method and ridge regression method to form a flexible regression network model, which is used to predict the amount of tourists and tourists’ demand for rural tourism so as to put forward the implementation path of the tourism industry to help rural revitalization development. To verify the effectiveness of the proposed algorithm, data analysis is carried out in rural areas of L city as an example. The results show that compared with the Lasso regression model, the average absolute percentage error decreased by 2.41%, the average absolute error decreased by 2,233, and the root-mean-square error decreased by 2,967, which indicates that the elasticity regression network prediction model has a stronger generalization ability and a better prediction ability, and it can predict the number of tourists more accurately, and it can provide data for the tourism industry to contribute to rural revitalization and development. Reference.

A Self-Adapting IoT Network Configuration Supported by Distributed Graph Transformations

The research described in this article aims to propose the creation of a framework that would enable the self-optimization of IoT device networks. The work is based on two foundations: distributed graph transformations and a flexible IoT network supported by the several standards and definitions proposed by The Open Group, such as the definition of microservices architecture (MSA) as well as IoT and semantic interoperability standards, providing a broader context for the research. It introduces the concept of capabilities, both at the individual device and network levels, which are used to describe the desired functions that will be performed by the given system. The network of distributed IoT devices is visualized as a graph, and graph transformations are used to specify and optimize the network in response to events like degraded performance, failures, or configuration changes. These actions are automatically performed in order to restore the original set of capabilities defined for the system. Validation of these capabilities is used to assess the success rate of the performed actions. The document describes a practical implementation of an IoT network for managing and monitoring an indoor greenhouse. Thanks to the introduction of formal representation of capabilities, the programming effort required to build the system was significantly reduced. Furthermore, automation related to the validation of capabilities and the performance of automated actions reduces the effort by a factor of a hundred compared with a manual action.

NDARTS: A Differentiable Architecture Search Based on the Neumann Series

Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML). Although search methods based on reinforcement learning and evolutionary algorithms can find high-performance architectures, these search methods typically require hundreds of GPU days. Unlike searching in a discrete search space based on reinforcement learning and evolutionary algorithms, the differentiable neural architecture search (DARTS) continuously relaxes the search space, allowing for optimization using gradient-based methods. Based on DARTS, we propose NDARTS in this article. The new algorithm uses the Implicit Function Theorem and the Neumann series to approximate the hyper-gradient, which obtains better results than DARTS. In the simulation experiment, an ablation experiment was carried out to study the influence of the different parameters on the NDARTS algorithm and to determine the optimal weight, then the best performance of the NDARTS algorithm was searched for in the DARTS search space and the NAS-BENCH-201 search space. Compared with other NAS algorithms, the results showed that NDARTS achieved excellent results on the CIFAR-10, CIFAR-100, and ImageNet datasets, and was an effective neural architecture search algorithm.

Simplified colorless characterization scheme for coherent receivers in DWDM scenarios using a single interferer

Intradyne coherent receivers, capable of detecting an individual wavelength-division multiplexed channel just by tuning the local oscillator frequency, is of great interest for the development of high-capacity flexible optical networks. Nevertheless, the unavoidable amplitude imbalances inherent to any realistic coherent receiver induce an interference contribution from the self-beating of the coincident channels present at its input. The characterization of this degraded colorless reception operation is of fundamental importance, but it usually requires the use of rather complex experimental setups, especially when the effects of tens of interference channels should be evaluated. In this work we propose a novel experimental setup that only requires the use of a single intense interferer to emulate those coincident channels, thus drastically simplifying the characterization process. In addition, we develop a general expression for the signal-to-noise ratio of the system that theoretically justifies the intended setup and demonstrate by massive numerical simulations its accuracy in different scenarios. We believe that the proposed approach may contribute to facilitate the experimental characterization of high-performance colorless coherent receivers.

Constructing flexible carbon-graphite network for enhancing durability of silicon anodes in Li-ion battery

Silicon-based materials still face critical issues including large volume expansion and poor electrical conductivity during the charging and discharging process. Despite the carbon coating strategy can improve performance, the practical operation of silicon-carbon anodes still suffers from undesirable cycling performance. Herein, we propose a novel method to construct Si/SiOx@G@C microparticles, where the Si/SiOx core is coated by conducive and flexible amorphous carbon networks and graphite shells. The Si/SiOx@G@C demonstrated a high discharge capacity of 821 mA h g−1 and an initial Coulombe efficiency of 82.5 % under the current density of 0.1A/g. At a current density of 1.0 A/g, the Si/SiOx@G@C electrodes delivered a high rechargeable capacity retention of 75.1 % and only 117 % volume expansion over 1,000 charge–discharge cycles. The demonstration of silicon-based batteries with a long cycle life is an important step towards the development of this field beyond Li-ion technology.

4Ward: A relayering strategy for efficient training of arbitrarily complex directed acyclic graphs

Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications. The graph underlying an MLP is indeed multipartite, i.e. each layer of neurons only connects to neurons belonging to the adjacent layer. In contrast, in vivo brain connectomes at the level of individual synapses suggest that biological neuronal networks are characterized by scale-free degree distributions or exponentially truncated power law strength distributions, hinting at potentially novel avenues for the exploitation of evolution-derived neuronal networks. In this paper, we present “4Ward”, a method and Python library capable of generating flexible and efficient neural networks (NNs) from arbitrarily complex directed acyclic graphs. 4Ward is inspired by layering algorithms drawn from the graph drawing discipline to implement efficient forward passes, and provides significant time gains in computational experiments with various Erdős-Rényi graphs. 4Ward not only overcomes the sequential nature of the learning matrix method, by parallelizing the computation of activations, but also addresses the scalability issues encountered in the current state-of-the-art and provides the designer with freedom to customize weight initialization and activation functions. Our algorithm can be of aid for any investigator seeking to exploit complex topologies in a NN design framework at the microscale.

Autonomous solution for Controller Placement Problem of Software-Defined Networking using MuZero based intelligent agents

Controller placement is a critical issue in Software-Defined Networking, which has been identified as a potential approach to achieve more flexible network control and management. Deep Reinforcement Learning holds immense promise in achieving favorable outcomes through its ability to explore solution spaces and adapt to swiftly changing data streams. Furthermore, the advancement of reinforcement learning algorithms, like chess or Go, has inspired the concept of treating controller placement problem as a Markov game. These algorithms are employed to train intelligent agents capable of autonomously resolving this problem. This paper presents an intelligent system capable of optimizing the placement of controllers in Software-Defined Networking. The Muzero Reinforcement learning algorithm is used to train a model via self-competition which, by combining a tree search with a learned model, achieves superhuman performance in complex domains. Once trained, the model is integrated with an Software-Defined Networking controller, so that it is able to find the optimal placement of the game in a real network by incorporating performance metrics including latency, load, and overhead communication into the training process of intelligent agents. In order to show that our approach is feasible and efficient in practice, experiment results are provided as a benchmark.

Day-ahead energy market model for the smart distribution network in the presence of multi-microgrids based on two-layer flexible power management

This study examines the implications of day-ahead energy markets (DA) for flexible power management (FPM) in a smart distribution network (SDN) with multiple microgrids (MMGs). MG sources are coordinated with MG operators (MGOs) in the first layer, and MGOs and SDN sources are coordinated with SDN operators in the second layer. An optimization framework with bilevels is used in the scheme. SDN participation in the wholesale and retail DA energy markets is represented by the upper-level model, equivalent to the second-layer FPM. Based on the network's linear operation and flexibility constraints and active load and source operation models, it minimizes the expected SDN energy cost in the markets mentioned. It has the same formulation as the upper-level problem but concerns MG's participation in the retail DA energy market. This corresponds to the first layer of FPM. After that, a single-level formulation is developed using the Karush-Kuhn-Tucker method, and stochastic programming models uncertainty about load, energy price, renewable generation power, and mobile active load energy demand. In this scheme, two-layer power management between microgrids and the distribution network; simultaneous modelling of economic, operation, and flexibility indices; and simultaneous participation of microgrids and the distribution network in the wholesale and retailer energy markets are the novelties. Ultimately, based on quantitative results, the proposed approach is evaluated in terms of its ability to improve the economic, operational, and flexibility state. The proposed scheme reduces the energy loss in the microgrids and the distribution network by around 43%-67% compared to power flow studies. Voltage drop in the proposed scheme is enhanced by about 40%-47% in the suggested scheme compared to power flow analysis. The economic status of the network is improved by roughly 22% in comparison with power flow studies. Also, the presented scheme can provide almost 100% flexibility conditions; however, it is corresponding to increased energy cost.

A large-scale heuristic approach to integrate on-demand warehousing into dynamic distribution network designs

A recent business model, on-demand warehousing, enables warehouse owners with extra distribution capacity to rent it out for short periods, providing firms needing flexible network designs a new type of distribution capacity. In this paper, a heuristic is created to solve large scale instances of dynamic facility location models that optimize distribution networks over a multi-period planning horizon, simultaneously considering the selection of different warehouse types with varying capacity granularity, commitment granularity, access to scale, and cost structures. The heuristic iteratively solves selected single-period problems, creating a set of smaller subproblems that are then solved for multiple periods. Their decisions are combined to achieve feasible low-cost solutions, ensuring each customer’s demand point is covered for each period. A set of computational experiments recommends how heuristic settings should be set by industrial decision makers and illustrates the heuristic can generate high-quality solutions for large scale networks during long planning horizons and many decision periods. The heuristic can solve national-level instances with many customer demand points, candidate locations, different warehouse types and capacity levels and many periods.