Network Operation Research Articles

Research on Low Power Methods for Wireless Devices in Intelligent Industrial Communication Systems

Industrial communication devices achieve low power consumption and high efficiency in WirelessHART networks through hardware and software optimization. Hardware advancements include advanced manufacturing processes, highly integrated chips, low-power components, dynamic power management, energy harvesting, and intelligent sensor technologies. On the software side, the network manager optimizes routing schedules, the data link layer adopts TDMA technology, and the protocol stack is tailored for specific scenarios. These measures ensure the network's efficient operation. Furthermore, the integration of smart devices enhances monitoring, control, and energy-saving capabilities, enabling real-time data analysis and decision-making. This fosters innovation and sustainability in industrial automation, paving the way for advanced analytics and seamless connectivity among different devices and systems in industrial environments.

High‐Bit‐Efficiency TOPS Optical Tensor Convolutional Accelerator Using Microcombs

AbstractTensor convolution is a fundamental operation in convolutional neural networks, especially for processing tensors, which are prevalent in real‐world applications. Current methods often convert tensor convolutions into matrix multiplications, leading to data replication, additional memory usage and increased hardware complexity. Here, a high‐bit‐efficiency optical tensor convolution accelerator with reduced data redundancy and lower memory consumption is presented. The bit‐efficiency of the optical tensor convolution accelerator is first explored, significantly improving its effective computing power by utilizing the spatial dimension. Consequently, the optical tensor convolutional accelerator operates at speeds exceeding 3 Tera Operations Per Second (TOPS)—the fastest single‐kernel optical convolutional accelerator to date, to the best of authors' knowledge. Its performance is validated on handwritten digit recognition and histopathologic cancer detection tasks, achieving 93.8% and 77% accuracy, respectively, closely matching in‐silico results. This approach simultaneously multiplexes the physical dimensions—wavelength, time, and space—and leverages the parallelism and high throughput of light, enabling efficient optical processing of tensor data with significant computational power.

Influence of Selected Parameters of Medium-Voltage Network on the Value of Voltage Asymmetry Factors

The correct and safe operation of electricity grids is a fundamental consideration in guaranteeing the supply of electricity with the correct parameters to consumers. One of the key aspects is how neutral point earthing works. In grids with the neutral point grounded by a Petersen coil, it is very important to properly tune its inductance in relation to the natural capacitance of the power grid. This is important both for the proper operation of earth fault protection and from the point of view of power quality, especially voltage asymmetry. Asymmetry of phase voltages in MV networks has a very negative impact on the operation of the railway network supplied by 12-pulse rectifiers. In this paper, the authors present the influence of the detuning factor of the earth fault compensation and the length and cross-section of the line on the value of voltage asymmetry factors. As the analyses carried out have shown, significant (up to 90%) values of the zero-sequence asymmetry factor can occur at low detuning, which can contribute not only to a decrease in the quality of the transmitted electricity but also to the unnecessary activation of protections. The values of the negative-sequence voltage asymmetry factor are much smaller (do not exceed 0.5% in the analysed cases) and do not depend on the network detuning factor. As the distance from the substation increases, an increase in the values of both types of asymmetry factors is also observed, with the relationship already negligibly small at considerable distances (above 20 km).

Potentials and Limitations of Using Sentinel Data for Power System Operation and Control: Case Study of Protection Against Forest Fires and Aerosol Contamination

The power network is directly or indirectly exposed to various weather and climate conditions that can positively and negatively influence it. Forest fires and various aerosol contaminations may negatively affect the power network. Surface observations and measurements (e.g., weather stations) help protect against unfavorable influences, but the network is usually poor. That challenge may be solved by including satellite data in the power network planning, operation, and maintenance. This article presents the possibilities of using Sentinel mission’s data, at various temporal and spatial levels, for those purposes through examples of protection against forest fires and aerosol contamination. In the case of forest fires, satellite data enable a more-precise calculation of forest fire-spreading danger for areas with no installed weather stations, which may be used in the power system contingency analysis calculation. The analysis showed that aerosol contamination has a correlation with outage appearance in the 25 kV 50 Hz electric traction network. Sentinel 5p data may serve as a tool in a surface air-quality measurement data control to confirm/deny the high concentration of PM10 particles. Analysis showed that Sentinel data are favorable for power network operations on intra-day and day-ahead operations, as well as for long-term operations, which include planning and maintenance. The spatial resolution of Sentinel data is more than enough to fulfill the surface weather station network. Satellite data also enable the use of additional data that are not measured at ground stations.

Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement

With the increasing penetration of distributed generation (DG), the supply–demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resources for regulation. This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. It replaces the traditional energy storage model with this model and then solves the EV and SOP collaborative planning model using a second-order conical planning algorithm with the objective function of minimizing the annual integrated cost. At the same time, the flexibility of the distribution network is analyzed by two indicators: power supply and demand balance and branch load margin. Finally, the optimization method proposed in this paper is analyzed and validated on an improved IEEE 33-node distribution system. Example results show that the planning method proposed in this paper can effectively reduce the annual comprehensive operating cost of distribution networks, meet the flexibility index, and be conducive to improving the economy and flexibility of distribution network operation.

Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management

Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.

Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP.

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.

Operation of spinal sensorimotor circuits controlling phase durations during tied-belt and split-belt locomotion after a lateral thoracic hemisection.

Locomotion is controlled by spinal circuits that interact with supraspinal drives and sensory feedback from the limbs. These sensorimotor interactions are disrupted following spinal cord injury. The thoracic lateral hemisection represents an experimental model of an incomplete spinal cord injury, where connections between the brain and spinal cord are abolished on one side of the cord. To investigate the effects of such an injury on the operation of the spinal locomotor network, we used our computational model of cat locomotion recently published in eLife (Rybak et al., 2024) to investigate and predict changes in cycle and phase durations following a thoracic lateral hemisection during treadmill locomotion in tied-belt (equal left-right speeds) and split-belt (unequal left-right speeds) conditions. In our simulations, the 'hemisection' was always applied to the right side. Based on our model, we hypothesized that following hemisection the contralesional ('intact', left) side of the spinal network is mostly controlled by supraspinal drives, whereas the ipsilesional ('hemisected', right) side is mostly controlled by somatosensory feedback. We then compared the simulated results with those obtained during experiments in adult cats before and after a mid-thoracic lateral hemisection on the right side in the same locomotor conditions. Our experimental results confirmed many effects of hemisection on cat locomotion predicted by our simulations. We show that having the ipsilesional hindlimb step on the slow belt, but not the fast belt, during split-belt locomotion substantially reduces the effects of lateral hemisection. The model provides explanations for changes in temporal characteristics of hindlimb locomotion following hemisection based on altered interactions between spinal circuits, supraspinal drives, and somatosensory feedback.

Operation of spinal sensorimotor circuits controlling phase durations during tied-belt and split-belt locomotion after a lateral thoracic hemisection

Locomotion is controlled by spinal circuits that interact with supraspinal drives and sensory feedback from the limbs. These sensorimotor interactions are disrupted following spinal cord injury. The thoracic lateral hemisection represents an experimental model of an incomplete spinal cord injury, where connections between the brain and spinal cord are abolished on one side of the cord. To investigate the effects of such an injury on the operation of the spinal locomotor network, we used our computational model of cat locomotion recently published in eLife (Rybak et al., 2024) to investigate and predict changes in cycle and phase durations following a thoracic lateral hemisection during treadmill locomotion in tied-belt (equal left-right speeds) and split-belt (unequal left-right speeds) conditions. In our simulations, the ‘hemisection’ was always applied to the right side. Based on our model, we hypothesized that following hemisection the contralesional (‘intact’, left) side of the spinal network is mostly controlled by supraspinal drives, whereas the ipsilesional (‘hemisected’, right) side is mostly controlled by somatosensory feedback. We then compared the simulated results with those obtained during experiments in adult cats before and after a mid-thoracic lateral hemisection on the right side in the same locomotor conditions. Our experimental results confirmed many effects of hemisection on cat locomotion predicted by our simulations. We show that having the ipsilesional hindlimb step on the slow belt, but not the fast belt, during split-belt locomotion substantially reduces the effects of lateral hemisection. The model provides explanations for changes in temporal characteristics of hindlimb locomotion following hemisection based on altered interactions between spinal circuits, supraspinal drives, and somatosensory feedback.

A novel fault prediction modeling and simulation method for power communication networks

The power communication network is the key to ensuring the safe operation of the distribution network, and how to quickly and accurately predict faults is a challenging task, especially considering the variable topology of the distribution network. To address this issue, common faults will be modeled and simulated to predict faults in power communication systems. In this study, considering the generalized Laplacian smoothing filters and the long sequence representation capability of the Transformer, an adaptive graph encoder based on historical performance graph embedding is proposed and used for fault prediction in power communication networks. The proposed method consists of two modules: (1) To better alleviate high-frequency noise in node features, a carefully designed Laplacian smoothing filter is first applied. (2) Adopting a transformer-based adaptive encoder to iteratively enhance filtering characteristics for better node embedding. The performance of the proposed method in fault prediction tasks is tested using a dataset collected from a real power communication network. The experimental results show that the proposed method consistently outperforms other fault prediction methods in terms of performance.

A Review of Distribution Network Expansion Planning

Distribution Networks Expansion Planning (DNEP) is very complex and involves improving the system to meet the increasing demand using the most cost-effective strategy. Among the planned choices are the extension of substations, upgrade of distribution feeders, installation of additional Distributed Energy Resources (DERs), installation of Capacitor Banks (CBs) and many other methods. Distribution planners in contemporary networks must have faith in the reversibility of investments where Renewable Energy Resources (RERs) inject clean and cost-effective for DNEP to meet growing demand and environmental requirements. The comprehensive review of DNEP carried out in this paper covers all possible objective functions and problem constraints. With the rise of electric vehicles (EVs), there is a growing need to assess the impact of EV charging on distribution networks. Understanding how EV loads affect the network helps plan future expansions to efficiently accommodate the charging infrastructure. Integrating DERs, such as solar panels, wind turbines, and energy storage systems, is changing how electricity is generated, distributed, and consumed. Assessing the integration of DERs into distribution networks is crucial for optimal network planning and operation. In addition, CBs are essential for power factor correction and voltage regulation in distribution networks. Including CBs in expansion planning helps improve network efficiency, reliability, and overall power quality. By analyzing the impact of EV loads, DERs, and CBs in DNEP, researchers and planners can develop more accurate models and strategies for designing sustainable and resilient power systems to efficiently meet the growing energy demands.

Scaling and networking a modular photonic quantum computer.

Photonics offers a promising platform for quantum computing1-4, owing to the availability of chip integration for mass-manufacturable modules, fibre optics for networking and room-temperature operation of most components. However, experimental demonstrations are needed of complete integrated systems comprising all basic functionalities for universal and fault-tolerant operation5. Here we construct a (sub-performant) scale model of a quantum computer using 35 photonic chips to demonstrate its functionality and feasibility. This combines all the primitive components as discrete, scalable rack-deployed modules networked over fibre-optic interconnects, including 84 squeezers6 and 36 photon-number-resolving detectors furnishing 12 physical qubit modes at each clock cycle. We use this machine, which we name Aurora, to synthesize a cluster state7 entangled across separate chips with 86.4 billion modes, and demonstrate its capability of implementing the foliated distance-2 repetition code with real-time decoding. The key building blocks needed for universality and fault tolerance are demonstrated: heralded synthesis of single-temporal-mode non-Gaussian resource states, real-time multiplexing actuated on photon-number-resolving detection, spatiotemporal cluster-state formation with fibre buffers, and adaptive measurements implemented using chip-integrated homodyne detectors with real-time single-clock-cycle feedforward. We also present a detailed analysis of our architecture's tolerances for optical loss, which is the dominant and most challenging hurdle to crossing the fault-tolerant threshold. This work lays out the path to cross the fault-tolerant threshold and scale photonic quantum computers to the point of addressing useful applications.

Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation

The increasing penetration of distributed generation (DG) brings about great economic and environmental benefits, while also negatively affecting the operation of distribution networks due to its high intermittency. Although distributed energy storage (DES) can effectively deal with the problems caused by massive DG penetrations by decoupling the generation and consumption of electricity, the placement of DES significantly determines the effectiveness of its capabilities. Unfortunately, existing DES placement studies are commonly based on a balanced network model, whereas practical distribution networks are unbalanced. In addition, existing DES placement studies are mostly based on an extreme scenario and rarely consider the operational complexity resulting from the uncertainties of DGs and loads. To address the aforementioned challenges, this paper proposes a hierarchical and sequential DES placement strategy in distribution networks by considering multi-scenario operations. Specifically, the proposed hierarchical framework for DES placement includes three sequential layers: outer, inter, and inner. In the outer layer, a multi-scenario comprehensive loss sensitivity index (MSCLSI) is first introduced to search for the most effective DES placement location. Subsequently, the sizing and scheduling of DES for the selected location are conducted through coordinated optimization across the inter and inner layers, which can be solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP). Finally, a series of detailed simulations are carried out over the IEEE-33 test system and the experimental results demonstrate that the proposed scheme can provide significant effectiveness and superiority compared to the state-of-the-art schemes.

Understanding of the OSI System Interaction Model for Organizing the Educational Process and Student’s Independent Work.

This paper discusses the principles of network operation from the point of view of the OSI model, which is useful for teaching IT students in organizing independent work of students. This helps to understand the interaction of network devices, processes at each level of the model and their impact on the performance and reliability of the network. Understanding the OSI model not only improves the quality of independent work of students, but also prepares them for successful professional activity in the rapidly developing field of network technologies

Prerequisites for the training of specialists in the operational phase of the life cycle of buildings, structures, and facilities

To ensure the normative service life of a building, structure, construction and to prevent premature reduction of technical, economic, ergonomic, aesthetic, and other characteristics of real estate objects, a specific set of engineering, organizational, economic, and other measures can be used (inspections, planning, technical regulations for maintenance, upkeep and repair, methods of studying and determining the condition of structural elements and engineering systems of buildings, structures and facilities). From the analysis of knowledge, skills, and abilities fixed in the sectoral professional standards the thesis is substantiated that the service of housing and communal services is derived from the actions of employees of enterprises of this sphere on their operation of buildings, structures, facilities, engineering equipment and networks. This paper provides justification for the formation of a new direction of training specialists to ensure effective functioning of buildings, structures, facilities at the stage of life cycle, i.e. operation. The competencies of a specialist engaged in organizing and carrying out works on maintenance of buildings, structures, and facilities are outlined.

Research on Typical Process Mini Programs for Overhead Distribution Lines based on Non Power Outage Operation Technology in Distribution Network

To address the challenges and risks associated with the complex design of distribution network architecture, uneven skill levels of construction personnel, and inadequate training of construction personnel, this article proposes a typical process mini program for overhead distribution lines based on uninterrupted operation of distribution networks. Through requirement analysis, the functions of the mini program were determined, and the front-end and system back-end functions of a typical process mini program for overhead distribution lines based on uninterrupted operation of the distribution network were designed. Based on the development architecture of the mini program and combined with cloud services, a typical process mini program for overhead distribution lines based on uninterrupted operation of the distribution network has been developed. The establishment of a typical process library and knowledge base for overhead distribution lines based on uninterrupted operation of the distribution network has been achieved, making it convenient for users to search, learn, and collect typical processes in a timely manner, and to learn and apply them to maintenance sites anytime, anywhere. The typical process mini program for overhead distribution lines based on uninterrupted operation of distribution network lays the foundation for better leveraging the technical advantages of uninterrupted operation of distribution network, further improving the reliability of distribution network power supply, and ensuring the accuracy of uninterrupted operation of distribution network.

Co-Optimization Operation of Distribution Network-Containing Shared Energy Storage Multi-Microgrids Based on Multi-Body Game.

Under the carbon peaking and carbon neutrality target background, efficient collaborative scheduling between distribution networks and multi-microgrids is of great significance for enhancing renewable energy accommodation and ensuring stable system operation. Therefore, this paper proposes a collaborative optimization method for the operation of distribution networks and multi-microgrids with shared energy storage based on a multi-body game. The method is modeled and solved in two stages. In the first stage, a multi-objective optimization configuration model for shared energy storage among multi-microgrids is established, with optimization objectives balancing the randomness of renewable energy fluctuations and the economics of each microgrid undertaking shared energy storage. The charging and discharging interactive power of energy storage and each microgrid at various time periods are obtained and passed to the second stage. In the second stage, with the distribution network as the leader and shared energy storage and multi-microgrids as followers, a game optimization model with one leader and 2 followers is established. The model is solved based on an outer-layer genetic algorithm nested with an inner-layer solver to determine the electricity purchase and sale prices among the distribution network, multi-microgrids, and shared energy storage at various time periods, thereby minimizing operational costs. Finally, based on the power interaction of microgrids to measure their contributions, an improved Shapley value cost allocation method is proposed, effectively achieving a balanced distribution of benefits among the distribution network, shared energy storage, and multi-microgrids, thereby improving overall operational revenue. Meanwhile, a new method for calculating the shared energy storage capacity and the upper limit of charging and discharging power based on a game framework was proposed, which can save 37.23% of the power upper limit and 44.89% of the capacity upper limit, effectively saving the power upper limit and capacity upper limit.

Radical translation at the ‘Break of Day’: Thomas Paine in a Celtic language

ABSTRACT This article presents the first detailed analysis of the ways in which border-crossing author and balladeer John Jones (pseudonym ‘Jac Glan-y-Gors’) remodelled a range of Thomas Paine’s writings into Welsh republican pamphlets by translating key passages, interpolating culturally relevant indigenous material, and consolidating Paine’s anti-monarchical core vocabulary. In doing so, the article provides a blueprint for examining the operation of intellectual networks who transferred ideas and cultural artefacts to smaller or non-hegemonic cultures and the process of embedding them. Jones’s work is analysed against the history of an early modern Welsh ‘translation culture’ that enabled a substantial expansion of radical translation activity, in tandem with the democratisation of the English press in the wake of the French Revolution of 1789, transferring Enlightenment and revolutionary artefacts, formats and ideas to Wales and the Welsh language. Using publishing networks that linked metropolitan London to rural Wales, an indigenous Welsh intelligentsia combined English models with indigenous cultural forms to produce a radical print culture in their own language. Jones’s republican pamphlets, the only substantial reception of Paine’s writings in Welsh, are read in the context of these processes.

A Dual-Path Neural Network for High-Impedance Fault Detection

High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our dual-branch network simultaneously processes both representations: the CNN extracts spatial features from the transformed images, while the GRU captures temporal features from the raw signals. To optimize model performance, we integrate the Crested Porcupine Optimizer (CPO) algorithm for the adaptive optimization of key network hyperparameters. The experimental results demonstrate that our method achieves a 99.70% recognition accuracy on a dataset comprising high-impedance faults, capacitor switching, and load connections. Furthermore, it maintains robust performance under various test conditions, including different noise levels and network topology changes.

Flexible Reconfiguration for Optimal Operation of Distribution Network Under Renewable Generation and Load Uncertainty

The primary objective when operating a distribution network is to minimize operating costs while taking technical constraints into account. Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. In this paper, a flexible, dynamic reconfiguration model is developed that enables a distribution network to minimize operating costs on an hourly basis. The model fitness function is to minimize the system costs, including power loss, voltage deviation, purchased power from the upstream network, renewable generation, and switching costs. The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. To evaluate the performance of the presented approach, it is validated on the IEEE 33-bus radial system and TPC 83-bus real system. The simulation results show the impact of dynamic reconfiguration on reducing operation costs. It is found that dynamic reconfiguration is an efficient solution for reducing power losses and total energy drawn from the upstream network by increasing the number of switching operations.