Network Control Research Articles

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

Functional alterations in overweight/obesity: focusing on the reward and executive control network.

Overweight (OW) and obesity (OB) have become prevalent issues in the global public health arena. Serving as a prominent risk factor for various chronic diseases, overweight/obesity not only poses serious threats to people's physical and mental health but also imposes significant medical and economic burdens on society as a whole. In recent years, there has been a growing focus on basic scientific research dedicated to seeking the neural evidence underlying overweight/obesity, aiming to elucidate its causes and effects by revealing functional alterations in brain networks. Among them, dysfunction in the reward network (RN) and executive control network (ECN) during both resting state and task conditions is considered pivotal in neuroscience research on overweight/obesity. Their aberrations contribute to explaining why persons with overweight/obesity exhibit heightened sensitivity to food rewards and eating disinhibition. This review centers on the reward and executive control network by analyzing and organizing the resting-state and task-based fMRI studies of functional brain network alterations in overweight/obesity. Building upon this foundation, the authors further summarize a reward-inhibition dual-system model, with a view to establishing a theoretical framework for future exploration in this field.

Towards explainable traffic signal control for urban networks through genetic programming

The increasing number of vehicles in urban areas draws significant attention to traffic signal control (TSC), which can enhance the efficiency of the entire network by properly switching the phases of each signalized intersection. Fixed and max-pressure methods are commonly used in TSC systems owing to their high simplicity and good interpretability, but they respectively lack dynamic adaptability and automatic rule generation, possibly leading to low solution accuracy in complicated traffic environments. Meanwhile, meta-heuristic and black-box learning methods meet challenges in practice such as extensive computational time and poor interpretability. To this end, this paper proposes a new TSC method based on Genetic Programming (GP) to generate descriptive score rules automatically for switching phases of all signalized intersections in an urban transportation network. In the proposed method, switching phases of each signalized intersection type is formulated as a symbolic regression problem, and effective primitives are defined to facilitate GP to solve the problem. Experiments have been conducted on both synthetic and real-world networks. The results have validated the effectiveness of our proposed GP based method compared to several state-of-the-art TSC methods in terms of accuracy and interpretability.

Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics

Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics

Design of Radial Distribution System to Study Load-Flow and Short Circuit Analysis Using ETAP Software

This work's analysis provides information on fault analysis and load flow studies, which help to understand how radial distribution networks operate both in normal and emergency circumstances. The research explores optimization methods, describing capacitor placement, transformer size, and feeder reconfiguration in order to lower losses and increase system efficacy. Moreover, the work looks at reliability-focused maintenance techniques, stressing the need of predictive maintenance and condition monitoring in raising the dependability of radial distribution networks. Modern electrical power networks depend heavily on their radial distribution system, which effectively and dependable distributes electricity to customers. Many radial distribution system-related topics are covered in detail on this website, including analytical methods, optimization processes, and dependability enhancement strategies. Power flow, voltage control, and losses in radial networks are investigated in this paper under a range of operational situations. The last and crucial stage of a power system, the electrical distribution system makes sure that customers have a steady supply of electricity. Regretfully, because it is often neglected and exposed to environmental risks, our distribution network has a number of problems. Our distribution feeder is rigorously analyzed for load flow and short-circuits in order to overcome these challenges and find and fix problems. The main topics of this study are the load flow and short-circuit analysis of a radial distribution feeder, which is mostly constructed with (ETAP). The final and crucial part of a power system, the electrical distribution system ensures that consumers receive an uninterrupted and steady supply of electricity. Our distribution feeder undergoes a rigorous and cautious process to address these issues front on because of the uncontrollable nature and susceptibility of our network to environmental hazards. This paper delves deeply into the issues of short-circuiting and load flow in radial distribution feeders. Both symmetrical and asymmetrical faults are thoroughly investigated by mathematical computations and ETAP simulations; the results of the software simulations are contrasted with those of the mathematical analysis. These discoveries will definitely influence the development and management of our system and point us in the direction of workable and practical solutions.

Analytical target cascading based real-time distributed voltage control for MV and LV active distribution networks

This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV&LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV&LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV&LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV&LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme.

Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique

This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.

Abnormal dynamic functional connectivity in young nondisabling intracerebral hemorrhage patients.

Previous resting-state functional magnetic resonance imaging studies on intracerebral hemorrhage patients have focused more on the static characteristics of brain activity, while the time-varying effects during scanning have received less attention. Therefore, the current study aimed to explore the dynamic functional network connectivity changes of intracerebral hemorrhage patients. Using independent component analysis, the sliding window approach, and the k-means clustering analysis method, different dynamic functional network connectivity states were detected from resting-state functional magnetic resonance imaging data of 37 intracerebral hemorrhage patients and 44 healthy controls. The inter-group differences in dynamic functional network connectivity patterns and temporal properties were investigated, followed by correlation analyses between clinical scales and abnormal functional indexes. Ten resting-state networks were identified, and the dynamic functional network connectivity matrices were clustered into four different states. The transition numbers were decreased in the intracerebral hemorrhage patients compared with healthy controls, which was associated with trail making test scores in patients. The cerebellar network and executive control network connectivity in State 1 was reduced in patients, and this abnormal dynamic functional connectivity was positively correlated with the animal fluency test scores of patients. The current study demonstrated the characteristics of dynamic functional network connectivity in intracerebral hemorrhage patients and revealed that abnormal temporal properties and functional connectivity may be related to the performance of different cognitive domains after ictus. These results may provide new insights into exploring the neurocognitive mechanisms of intracerebral hemorrhage.

Multi-time-scale voltage control of the distribution network with energy storage equipped soft open points

The integration of distributed generation (DG) units into distribution networks (DNs) has brought about several operational challenges, including voltage issues and increased power loss. Energy storage equipped soft open points (E-SOPs) can accurately and flexibly control active and reactive power flows to address these problems. Additionally, the photovoltaic (PV) inverter and the network reconfiguration (NR) play a significant role in voltage control by adjusting the reactive power and the topology of the DN, respectively. However, due to differences in response times, there is a lack of systematic coordination between NR and the inverters of the E-SOP and PV. This paper proposes a multi-time-scale voltage control model that includes day-ahead NR scheduling, inter-day droop control optimization of the PV and E-SOP, and real-time local droop control. Considering the uncertainties of renewable DG outputs and loads, a robust optimization method is used in the day-ahead stage to obtain a reliable network structure. Then, with more accurate intra-day predictions, a stochastic optimization method is used to obtain the optimal state-of-charge interval, aiming to provide a flexible regulation range for battery energy storage to cope with the power fluctuations during the real-time stage. In addition, to address the intra-day voltage control model with bilinear constraints of the droop control function, a particle swarm optimization method is used. The results are verified on a 33-bus DN system through comparative analyses, showing effective performance.

Real-time quality assurance and quality control for a high frequency radar network

Real-time quality assurance and quality control for a high frequency radar network

An online updated linear power flow model based on regression learning

AbstractThe linear power flow (LPF) model is widely used in the optimization, operation, and control of distribution networks. These applications require the LPF model to be accurate, fast, and simple in order to simplify calculations as well as to efficiently perform operations and scheduling. In addition, it is difficult to realize the online update of parameters in the existing LPF models. The model retraining brings serious data burden and inefficiency. To serve these applications and comply with requirements, a brand new LPF model is proposed in this paper. A quadratic power flow model is trained by regression learning first, and then the proposed LPF model is derived by Taylor expansion. After only one initial regression learning, the proposed LPF model no longer needs retraining when updated. The refreshed parameter is simply updated online according to the real‐time measurement data, which improves the generalization ability. In conclusion, the proposed LPF model is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies these superiorities.

Integrated charging scheduling and operational control for an electric bus network

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. Operational control is carried out through speed control of the vehicles and bus holding at the terminal. We propose a hierarchical control framework to solve this integrated problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules.

Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system.

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks

Joint Energy-Efficiency Communication Optimization and Perimeter Traffic Flow Control for Multi-Region LTE-V2V Networks

Contagion dynamics in time-varying metapopulation networks with node's activity and attractiveness.

The metapopulation network model is a mathematical framework used to study the spatial spread of epidemics with individuals' mobility. In this paper, we develop a time-varying network model in which the activity of a population is correlated with its attractiveness in mobility. By studying the spreading dynamics of the SIR (susceptible-infectious-recovered)-type disease in different correlated networks based on the proposed model, we theoretically derive the mobility threshold and numerically observe that increasing the correction between activity and attractiveness results in a reduced mobility threshold but suppresses the fraction of infected subpopulations. It also introduces greater heterogeneity in the spatial distribution of infected individuals. Additionally, we investigate the impact of nonpharmaceutical interventions on the spread of epidemics in different correlation networks. Our results show that the simultaneous implementation of self-isolation and self-protection is more effective in negatively correlated networks than that in positively correlated or non-correlated networks. Both self-isolation and self-protection strategies enhance the mobility threshold and, thus, slow down the spread of the epidemic. However, the effectiveness of each strategy in reducing the fraction of infected subpopulations varies in different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. Our study will provide insights into epidemic prevention and control in large-scale time-varying metapopulation networks.

Six sigma improves the performance of quality control, financial and operation efficiency in a nationwide laboratory network in Mexico

Six sigma improves the performance of quality control, financial and operation efficiency in a nationwide laboratory network in Mexico

Asymptotic Stability Analysis and Stabilization Control for General Fractional-Order Neural Networks via an Unified Lyapunov Function

Asymptotic Stability Analysis and Stabilization Control for General Fractional-Order Neural Networks via an Unified Lyapunov Function

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

Optimal Volt/Var Control for Unbalanced Distribution Networks With Human-in-the-Loop Deep Reinforcement Learning

Optimal Volt/Var Control for Unbalanced Distribution Networks With Human-in-the-Loop Deep Reinforcement Learning