Transmission Capacity Of Networks Research Articles

Synchronization of Lurie Systems Under Limited Network Transmission Capacity With Quantization and One-Step Packet Dropout: An Active Method

This article considers the synchronization problem of drive–response Lurie systems with sampled output error transmitted through a limited network channel with a one-step packet dropout. Two kinds of strategies, that is, quantizer-based triggered control (without packet dropout) and active quantizer and packet dropout-based triggered control (with one-step packet dropout), are put forward. By thoroughly exploring quantizer, sampling interval, and one-step packet dropout information and merging them together, the quantizer and packet dropout related triggering method is proposed to actively deal with the negative effects caused by packet dropout and sampling sensor. With the proposed method, it is demonstrated that synchronization can be ensured and the output error will always be bounded by the quantizer range. In addition, the relationship between the sensor sampling interval and the triggering parameter is provided to match up with our proposed methods. Lower transmission bit rate is also obtained to save more channel resources. Synchronization of two Chua’s circuits is given as an example to demonstrate the validity of our presented results.

Information Transmission Capacity and Robustness of Natural Resource Governance Networks in Brazil and Indonesia: A Comparative Analysis

Information Transmission Capacity and Robustness of Natural Resource Governance Networks in Brazil and Indonesia: A Comparative Analysis

Investigation of advanced control for unified power flow controller (UPFC) to improve the performance of power system

Recently, global power demand has significantly increased. As this demand does not have proportional impact with transmission capacity and power generation. The new techniques are used to improve power system performance. The Flexible Alternating Current Transmission System (FACTS) devices are applied to improve the existing transmission network capacity. The Unified Power Flow Controller (UPFC) has been selected in this study to control power flow in transmission networks and improve the power system operation. In this paper, the Artificial Neural Network Controller (ANN) proposed to overcome the problems of the existing UPFC controller and tested using the IEEE-14 bus system with various test cases. The results showed that the proposed ANN has improved the efficiency of UPFC successfully by increasing the active power flow, reducing reactive power, and improving the voltage profile. The performance of proposed ANN-based UPFC has also been compared with Proportional Integral (PI) based UPFC and with Fuzzy Logic Controller (FLC) based UPFC shows its effectiveness. The simulation results have shown that the proposed ANN-based UPFC proved its robustness in improving all power system parameters compared to PI-based UPFC.

Investigation of advanced control for unified power flow controller (UPFC) to improve the performance of power system

Recently, global power demand has significantly increased. As this demand does not have proportional impact with transmission capacity and power generation. The new techniques are used to improve power system performance. The Flexible Alternating Current Transmission System (FACTS) devices are applied to improve the existing transmission network capacity. The Unified Power Flow Controller (UPFC) has been selected in this study to control power flow in transmission networks and improve the power system operation. In this paper, the Artificial Neural Network Controller (ANN) proposed to overcome the problems of the existing UPFC controller and tested using the IEEE-14 bus system with various test cases. The results showed that the proposed ANN has improved the efficiency of UPFC successfully by increasing the active power flow, reducing reactive power, and improving the voltage profile. The performance of proposed ANN-based UPFC has also been compared with Proportional Integral (PI) based UPFC and with Fuzzy Logic Controller (FLC) based UPFC shows its effectiveness. The simulation results have shown that the proposed ANN-based UPFC proved its robustness in improving all power system parameters compared to PI-based UPFC.

Structure-Level 3D Building Model Encoding Method for Progressive Transmission

Progressive encoding and transmission, i.e., a crucial technical foundation of 3D Web Geographic Information Systems (WebGIS), addresses the contradiction between massive 3D building data and limited network transmission capacity. Most progressive encoding algorithms, taking vertices, edges or triangles as encoding units, may break the inherent geometric and topological characteristics of 3D building models. Thus, a novel 3D building model encoding method that can maintain the internal characteristics is proposed, which can be used for high-efficiency progressive transmission. With this method, each building is decomposed into three types of fundamental structures: main structure, independent structure and attached structure. A structural topology graph (STG) was constructed based on the connections among structures. Guided by STG, one or more structures were wrapped as the smallest incremental transmission unit, denoted as transmission node. When requested, the real-time position of viewpoint, orientation and visual importance of nodes are used to pick up expected nodes for responding. The results confirm that the proposed method can better maintain the geometric and topological characteristics while encoding 3D building models. While serving for transmission, the proposed method not only effectively reduces the transmission load, but also provides users with a better consistency experience on the building appearance at different simplification levels.

Multi-Band Elastic Optical Networks: Inter-Channel Stimulated Raman Scattering-Aware Routing, Modulation Level and Spectrum Assignment

Recently, multi-band elastic optical networks (MB-EONs) have been introduced to increase the transmission capacity of optical networks by expanding the transmission frequency range beyond the conventional C-band (e.g., O-, E-, S-, and L-band). Thus, by utilizing the idle frequency bands of the existing standard single mode fibers (SMFs), the capacity improvement in MB-EONs is achieved with lower capital expenditure compared to other techniques, such as space division multiplexing (SDM) in multi-core fibers (MCFs). The routing, modulation level and spectrum assignment (RMLSA) problem in MB-EONs is more challenging than that of the conventional single band EONs, and the inter-channel stimulated Raman scattering (ISRS) process should be considered in signal-to-noise ratio (SNR) analysis. In this article, ISRS-aware RMLSA problem in MB-EONs is studied. We obtain the optimal solution of this problem by formulating a path-based integer linear programming (ILP) model. Since the number of slots and demands are too large in a multi-band system, the ILP problem is too complex to be solved in polynomial time. Therefore, we propose heuristic algorithms to solve the ISRS-aware RMLSA problem. Then, performance of the proposed schemes is evaluated through exhaustive simulations. The results reveal that among all the proposed heuristic algorithms, the algorithm named as enhanced in-process estimation (EIPE) has the best performance in saving more frequency slots for future demands, while achieving higher spectral efficiency and SNR estimation accuracy. Moreover, for a special case of small-scale scenario, the performance of EIPE is close to the results of ILP formulation. Finally, our results indicate that by ignoring ISRS process in the SNR estimation, outage occurs for some of lightpaths, where the outage percentage depends on the launch power.

An Optimal Decision-Making Method for Power Market Transaction Based on Renewable Energy Multi-Scenario Forecast

In order to improve the adaptability of power market transaction to the uncertainty of renewable energy, the multi-scenario forecast model of renewable energy is studied and an optimal decision-making method for power market transaction based on renewable energy multi-scenario forecast is proposed. Multi-scenario forecast of renewable energy is a forecast result composed of multiple forecast curves, which is an uncertain forecast model in nature. The optimal power market transaction decision-making method takes the lowest expected cost and the lowest loss of renewable energy as the optimization objectives and comprehensively considers the constraints including power balance, power network transmission capacity and so on. Finally, a case study based on IEEE-30 buses system shows that this method can fully consider the impact of renewable energy uncertainty on trading results and has a significant effect on improving the efficiency of power market transaction results.

Construction and Modeling of Multi-Circuit Multi-Voltage HVAC Transmission Lines

A transmission network’s main objective is to continuously supply electrical energy to consumers. This article presents an analysis of the use of multi-circuit, multi-voltage overhead lines as a compromise between ensuring the system’s safe operation by increasing the transmission network capacity and managing the constraints related to its expansion. The considerations presented in this work include the construction of such lines, their operation, and modeling aspects. As part of the study, the potential for improving the environmental conditions around the lines is discussed in terms of the necessary area for their construction and the peak electromagnetic field strength in their vicinity. We also present a mechanical analysis of stress and sag coordination in the individual circuits of these lines. Then, we detail the method for determining the electrical parameters of multi-voltage lines’ series impedances and capacitance. Specific attention is given to the possibility of zero-sequence voltage that occurs in the systems despite the symmetric supply and load of circuits—especially in the circuits with the lowest voltages—that result from the line’s geometric asymmetry. We evaluate the impact of the line’s geometric asymmetry on the power system’s correct operation by determining the asymmetry factors. Finally, the accuracy of using a simplified symmetric model for lines with various geometric asymmetries is analyzed by studying the error of the short-circuit currents.

Equal Area Partition‐Based Energy Efficient Routing Algorithm for Circular WSN

Wireless sensor network (WSN) is communicating method with each other without the assistance of communication cable. The circular environment is an important application scenario in WSN. As a core technology, routing algorithm is the brain of WSN monitoring system. In this paper, we propose a new routing algorithm named Equal Area Partition Based Energy Efficient Multipath Algorithm (EAPEEM), which tries to exploit sector equal area model in node deployment, data transfer, and route maintenance. Existing algorithms, as compared to our existing models, do not fully match with routing and nodes. One attractive feature of our protocol is its balanced network transmission capacity and improved node energy utilization, which can reduce the possibility of system damage. Simulations show that EAPEEM can retain at least 28.6% of batteries and expand stability by more than 43%.

Optimization of transmission capacity of energy water pipeline networks with a tree-shaped configuration and multiple sources

The problem of pipeline network transmission capacity optimization is solved during network design, optimization, and development. It consists in determining the optimal pipeline diameters, installation sites, and parameters of pumps and valves. This study proposes a methodology for optimizing the transmission capacity of tree-shaped water pipeline networks of energy systems that serve various purposes and have multiple sources. A method for constructing a network model that ensures the versatility of its representation regardless of the purpose of the network and the composition of its equipment is proposed. A method for modeling of the branch that enables one to flexibly alter the configuration of the branch in the process of running the optimization algorithm without changing the network topology is presented. The mathematical statement of the problem is formulated as the discrete-continuous optimization problem. This statement is based on the proposed methods of representing the network model and its elements. An algorithm based on dynamic programming is proposed, which implements a new approach to setting up the computational procedure and is versatile enough for calculating networks that serve various purposes. The results obtained during real network optimization are presented.

A Structural Characterization of Market Power in Electric Power Networks

We consider a market in which capacity-constrained generators compete in scalar-parameterized supply functions to serve an inelastic demand spread throughout a transmission constrained power network. The market clears according to a locational marginal pricing mechanism, in which the independent system operator (ISO) determines the generators' production quantities to minimize the revealed cost of meeting demand, while ensuring that network transmission and generator capacity constraints are met. Under the stylizing assumption that both the ISO and generators choose their strategies simultaneously, we establish the existence of Nash equilibria for the underlying market, and derive an upper bound on the allocative efficiency loss at Nash equilibrium relative to the socially optimal level. We also characterize an upper bound on the markup of locational marginal prices at Nash equilibrium above their perfectly competitive levels. Of particular relevance to ex ante market power monitoring, these bounds reveal the role of certain market structures---specifically, the \emph{market share} and \emph{residual supply index} of a producer---in predicting the degree to which that producer is able to exercise market power to influence the market outcome to its advantage. Finally, restricting our attention to the simpler setting of a two-node power network, we provide a characterization of market structures under which a Braess-like paradox occurs due to the exercise of market power---that is to say, we provide a necessary and sufficient condition on market structure under which the strengthening of the network's transmission line capacity results in the (counterintuitive) increase in the total cost of generation at Nash equilibrium.

Minimizing Inter-Core Crosstalk Jointly in Spatial, Frequency, and Time Domains for Scheduled Lightpath Demands in Multi-Core Fiber-based Elastic Optical Network

Elastic optical networks (EON) technology in combination with space division multiplexing (SDM) is considered as having the potential to expand the transmission capacity of optical transport networks. However, inter-core crosstalk may cause serious signal impairment in a multi-core fiber (MCF) links. At the same time, scheduled lightpath demands, for which the expected setup and teardown times are known in advance, are considered as an important type of traffic demand for future networks. In this article, we develop approaches to schedule simultaneous lightpaths onto non-adjacent MCF cores so as to reduce inter-core crosstalk between these lightpaths. To this end, we first define a new metric to estimate the inter-core crosstalk jointly considering the spatial, frequency, and time domains. We then tackle the routing, spectrum, core, and time assignment (RSCTA) problem for the MCF-based EON by developing an integer linear programming (ILP) model, as well as an auxiliary graph (AG) based heuristic algorithm, which jointly optimize spectrum resource utilization and reduce the lightpath inter-core crosstalk. Simulation studies show the effectiveness of the proposed approach in terms of both performance aspects. In addition, the performance of the proposed heuristic algorithm is shown to be close to that of the ILP model in small networks.

Lightening the load: quantifying the potential for energy-efficient lighting to reduce peaks in electricity demand

One of the key challenges to greater renewable electricity supplies is the temporal mismatch between non-dispatchable renewable sources and peaks in electricity demand. In addition, increased electrification coupled with the de-carbonisation of electricity generation is likely to increase the scale of demand peaks. This could force investment in carbon-intensive peaker generation or capital intensive storage capacity as well as additional transmission and distribution network capacity which may then be substantially underutilised. Whilst considerable effort has been devoted to testing a range of demand response interventions to reduce or shift consumption, less attention has been given to the ability of certain appliances to permanently reduce demand at peak through energy efficiency. In this paper, we use a published model of future energy-efficient lighting uptake together with multi-year measured lighting demand data from a sample of residential households to model the potential power (MW) and energy (MWh) reductions of a ‘business as usual’ rate of efficient lighting adoption. Our estimates suggest that whilst lighting comprises ~ 4% of overall New Zealand annual electricity consumption, it comprises up to 12% of evening peak electricity consumption in winter. As a result, we estimate that by 2029, more efficient residential lighting could reduce New Zealand’s total annual demand by 1 TWh and reduce the highest winter evening peaks (at 17:00) by at least 500 MW (9%). The winter evening demand reduction would be roughly equivalent to avoiding the need for additional generation capacity of the scale of New Zealand’s Huntly Power Stations 1–4 (coal/gas) plus the Stratford peaker plant (gas open-cycle) and has clear implications for any electricity system that is intending to transition towards ~ 100% renewable generation at least cost.

Optimisation of the transmission capacity of cognitive networks

Cognitive radio technology enables two wireless networks, namely a primary network and a secondary network, to operate in the same geographic region sharing the same spectrum. Primary users have priority to access the spectrum without taking into account the presence of secondary users, while secondary users have to limit their interference in order not to disturb the operation of primary users. This work analyses a cognitive network in which the secondary network can adapt its parameters, in order to exploit any available opportunity for secondary capacity improvement, under the restriction that these adjustments cannot cause any additional harm to primary users. Using Poisson point process modelling, the authors present a closed-form formulation to adjust the secondary modulation order, user density, and transmit power, in order to maximise the secondary capacity, without causing any additional disturbance to primary users, and keeping the quality of secondary transmissions above a minimum acceptable level. The formulation proposed allows us to gain meaningful insights regarding the interrelationship involving some network parameters and performance metrics.

Novel crosstalk, fragmentation-aware algorithms in space division multiplexed- Elastic Optical Networks (SDM-EON) with considering physical layer security

One of the newest technologies to qualify transmission capacity of optical backbone networks is Space Division Multiplexing with Elastic Optical Networks (SDM-EONs). SDM-EON has the feasibility to add flexibility to Wavelength division Multiplexing (WDM) networks. In SDM-EON, connections can be setup more efficiently than WDM networks and EON without SDM. There are challenging issues in SDM-EON as fragmentation (created due to dynamically allocating and releasing allocated bandwidth of connections over time), impairments and physical layer security vulnerabilities caused by inter-core crosstalk. In this article, we introduce three novel algorithms to resolve crosstalk and maintain physical layer security level and reduce blocking probability in SDM-EON as called Minimum Crosstalk by fragmentation per cores (Min_Cross), Minimum Fragmentation by Crosstalk per cores (Min_Frag), and Moderate fragmentation and crosstalk (Mod_Frag_Cross). We consider multi-core fibers (MCFs) in EON and study how to setup lightpaths by considering the impairments and physical layer security leaks and attacks caused by inter-core crosstalk. We investigate spectrum, and core assignment algorithms to control physical-layer security attacks in order to reduce inter-crosstalk impairments as well as reducing Blocking Probability (BP) as our performance evaluation results show.

Self-Adaptive Resource Allocation in Underwater Acoustic Interference Channel: A Reinforcement Learning Approach

Since underwater acoustic channels are shared by multiple heterogeneous entities and can suffer from severe interference, underwater acoustic communication networks (UACNs) are faced with the challenge of mitigating interference and improving communication quality by implementing distributed resource allocation approaches. In this article, we introduce the concept of reinforced learning in intelligent control to the UACNs by treating the nodes as intelligent agents and the node networks as multiagent networks. By partitioning the state space and the action space, we formulate a reward function and a search strategy and propose a distributed resource allocation algorithm based on cooperative $Q$ -Learning. In addition, we verify the convergence of the proposed algorithm. Finally, simulation results in two different underwater application scenarios show that the proposed algorithm outperforms the existing algorithms in improving the network transmission capacity, and can reduce the overhead of resource allocation by using cooperative $Q$ -Learning.

A New Probability Evaluation Model and Method of Wind Power Absorption Capability Based on Multi-Scenario Operation Simulation

This paper proposed a probabilistic evaluation method of wind power absorption capacity considering interregional interaction based on multi-scenario operation simulation. The time scale of this method is pre-day, and many factors related to wind power absorption can be considered comprehensively, such as peak shaving capacity, frequency modulation capacity, load tracking capacity, rotating reserve capacity and network transmission capacity, which can provide a Multidimensional ratio evaluation of wind power absorption capacity which could identify the short-board factors that restrict wind power absorption. This paper provides a general framework for wind power absorptive capacity assessment. In the practical application of power system, the constraints can be flexibly chosen according to their own characteristics. The analysis results of the above examples show that the proposed method can effectively consider the inter-regional interaction, and can simulate and evaluate the system more accurately.

A MDP-Based Vulnerability Analysis of Power Networks Considering Network Topology and Transmission Capacity

This paper aims to study the vulnerability of the network to sequential cascading failures attacks where the attack strategy integrates network theory and discounted reward with Markov decision process (MDP) in the target selection process. A control strategy is designed to maximize the attack's long-term expected reward while reducing the attack sequence duration. The attack model identifies the most suitable targets by prediction through a Markov process for predicting the propagation and consequences of the failure. The state transition probabilities through a hidden failure model embedded in an independent edge-dependent network evolution model is estimated. Value iteration algorithms are used to identify targets at every attack stage. Target selection is updated depending on network changes. The results provide an optimal attack strategy based on network congestion with maximum damage, considering congestion as a cascade propagation mechanism. Reward functions based on increasing congestion and immediate power loss are compared. Strategies designed with network congestion as the attack reward function produce more vulnerability of the network to sequential attacks.

Delivering capacity allocation strategy for traffic dynamics on scale-free networks

The traffic dynamics of complex networks are largely determined by the node’s resource distribution. In this paper, based on the shortest path routing strategy, a node delivering capacity distribution mechanism is proposed into the traffic dynamics in Barabási and Albert (BA) scale-free networks; the efficiency of the mechanism on the network capacity measured by the critical point ([Formula: see text]) of phase transition from free flow to congestion is primarily explored. Based on the proposed strategy, the total delivering capacity is reallocated according to both degree and betweenness of each node, and an optimal value of parameter [Formula: see text] is found, leading to the maximum traffic capacity. The results of numerical experiments on scale-free networks suggest that the resource allocation strategy proposed here is capable of effectively enhancing the transmission capacity of networks. Furthermore, this study may provide novel insights into research on networked traffic systems.

Improvement of the Intelligent Tutor by Identifying the Face of the E-Learner's

As part of our project which aims at the realization of a system named ASTEMOI In this article, we display a new and productive facial image representation based on the Local Sensitive Hash (LSH). This technique makes it possible to recognize the learners who follow their training in our learning platform. Once recognized, the student must be oriented towards an appropriate profile that takes into account his strengths and weaknesses. We also use a light processing module on the client device with a compact code so that we don’t need a lot of bandwidth, a lot of network transmission capacity to send the feature over the network, and to be able to index many pictures in a huge database in the cloud.