Distributed Communication Network Research Articles

Channel prediction method based on the data-driving for distribution automation main station

A data-driven channel prediction method for distribution automation master is proposed to address the poor quality of communication network and communication system transmission problems in distribution network communication. In this paper, an adaptive broad learning network (ABLN) consisting of a standard broad learning network and a hybrid learning network is introduced to predict the channel state information of the communication system. Among them, the hybrid learning network is used to solve the ill-conditioned solution problem when estimating the output weight matrix of the standard broad learning network. Therefore, the ABLN produces sparse output weight matrices and provides excellent prediction performance. In the simulation analysis, the outdoor and indoor scenes are considered based on OFDM system. The prediction performance of ABLN is subsequently evaluated in one step prediction and multistep prediction. The results show that for the prediction performance is concerned, the maximum improvement of ABLN is about 96.49% as compared to other evaluation models, indicating that the CSI is effectively predicted by the ABLN to support the adaptive transmission of the main station of the distribution automation and to satisfy the quality of the communication network of the distribution network.

Comparative Analysis of Options for Organizing Internet Traffic Exchange in Territorially Distributed Communication Networks

Comparative Analysis of Options for Organizing Internet Traffic Exchange in Territorially Distributed Communication Networks

Comparative analysis of options for organizing internet traffic exchange in territorially distributed communication networks - Review 2

Comparative analysis of options for organizing internet traffic exchange in territorially distributed communication networks - Review 2

Comparative analysis of options for organizing internet traffic exchange in territorially distributed communication networks - Review 3

Comparative analysis of options for organizing internet traffic exchange in territorially distributed communication networks - Review 3

Applied Research on Intelligent Collection System of Positioning Information for Multi-source Heterogeneous Distribution Communication

Abstract In order to better realize the intelligent perception of fault location information of power distribution communication access networks in different scenarios, this paper carries out the construction of a power distribution communication network system through industrial Ethernet technology combined with EPON technology under fiber optic communication technology. Based on GIS technology and combined with OTDR technology, we constructed a fault location system for the fiber optic cable of the power distribution communication network so as to realize the acquisition of fault location information. From the multi-source state information, the joint Kalman filtering model is used to excavate the characteristics of the grid operation state, and then the fusion of multi-source heterogeneous data of power distribution communication is realized. To verify the effectiveness of the distribution communication network system constructed in this paper for locating fault information and intelligently collecting power consumption, system tests were conducted. The results show that the relative error of the node voltage estimation of the joint Kalman filter algorithm is below 0.015%, which improves the accuracy of fault identification in the distribution network. The system constructed in this paper for information transmission connectivity testing PON port light-emitting power range of 2~6dm, receiving optical power greater than -25dm, each PON port without power overload. This shows that the distribution automation communication network constructed in this paper meets the information collection requirements, and the construction of an intelligent distribution communication network can meet the requirements of distribution automation and can also accurately collect the fault location information of the distribution network.

Neural network-based fiber optic cable fault prediction study for power distribution communication network

Abstract As the foundation of communication networks, optical fiber carries huge network traffic, so the prediction of fiber optic cable faults is an important guarantee for the operation of communication networks. Based on the combination of fiber optic system networking technology and network management data, this study constructs an alarm correlation analysis method by using data mining technology to obtain the data set of the fault prediction model for the problem of low fault prediction accuracy of traditional communication networks. The dataset is used to balance the sample data by generating a small number of new samples through the generative adversarial network. The memory-based feature generation convolutional network is proposed to enhance the feature interaction to realize fault prediction in communication networks. The prediction model has a high prediction accuracy of 98.68%, which saves about 160 min for repair work through the application of fiber optic cable fault prediction, which compares well with other models. Fault prediction based on neural networks can provide assistance in the operation and maintenance of distribution communication networks.

Economic evaluation for 5G planning of distribution network considering transmission loss and vulnerability

Aiming at the difficulty of existing 4G networks to meet distribution network services, and the unclear economics of 5G in distribution network applications, an evaluation method of 5G communication planning economics for distribution networks considering the path loss model and comprehensive node vulnerability is proposed. Firstly, the exact coverage radius of 5G base stations is solved according to the signal transmission path loss model, and the objective function for solving the number and location of communication base stations in the distribution network is established. Secondly, a comprehensive communication node vulnerability evaluation model is established to judge whether each communication node meets the node vulnerability requirements under different communication modes, and to improve the communication reliability by installing additional fiber optic or micro base stations for nodes with too much vulnerability. Meanwhile, a comprehensive cost model for communication planning is established. Finally, the distribution network communication planning economic evaluation model is established. To verify the effectiveness of the method in this paper, the IEEE-123 topology model is used as an example to compare the economic evaluation of 5G and 4G in three scenarios: main urban areas, county town and township, respectively. According to the simulation results, using 5G communication in the distribution network reduces the total investment, operation and maintenance costs of the main urban area by 1.64 million yuan compared to 4G within 10 years. The total investment and maintenance cost of 5G in the county town for 10 years decreased by 2.16 million yuan compared to 4G. The cost of 5G in the township scenario is reduced by 860,000 yuan compared to 4G. According to the results of the comprehensive economic comparison, the comprehensive economic performance of 5G in the three scenarios is lower than that of 4G in the first few years due to the investment cost. In comparison, the comprehensive economic performance of 5G is better than that of 4G in the subsequent time due to the unique obtainable benefits of 5G, which concludes that 5G is better than that of 4G communication in all cases.

Multi-Stage Decoding in Dense Distributed Networks for MTC

Dense distributed networks have been proposed to fulfil the wireless connectivity demands in the 5th generation (5 G) and beyond. In this paper, a multi-stage decoding (MSD) scheme is proposed to enhance access connectivity for machine-type communication (MTC) in dense distributed networks. Different from the conventional single-stage decoding (SSD) scheme that performs local decoding at each access point (AP) independently, the MSD scheme exploits the local decoding information exchange between the APs and a central processing unit (CPU) to resolve access collisions and enhance access capacity with successive interference cancellation (SIC). To show the performance superiority of MSD to SSD, we analytically evaluate and compare the error probabilities of both the schemes by leveraging on techniques from stochastic geometry. Simulation results verify our analysis and indicate that the MSD scheme is able to effectively reduce the error probability and afford much higher access load compared to the SSD scheme, which makes it more suitable for massive MTC.

Cooperative Output Regulation of Large-Scale Wind Turbines for Power Reserve Control

This paper develops a distributed control framework to coordinate large-scale wind turbines for fairly sharing the power reserve request on a wind farm. A cooperative output regulation problem is formulated in a multi-agent framework, where the synchronization of turbine system outputs are established regarding the heterogeneity of turbine models. For this purpose, a homogeneous virtual system is created first and embedded into each turbine to generate the agreed trajectories across the wind farm in a distributed manner. The local power tracking of individual turbines is then enforced by the synthesized output regulator, whose control gains are determined by a variant of the Sylvester equation. The proposed approach issues the generator torque and blade pitch commands in a unified manner and is resistant to wind disturbances. The case studies are carried out through simulations and in a real laboratory-scale distributed communication network. The simulation results demonstrate the effectiveness of coordinated actions of wind turbines in response to the changing demands, under both constant and turbulent wind scenarios with the plug-and-play capability.

An unsupervised cyberattack detection scheme for AC microgrids using Gaussian process regression and one‐class support vector machine anomaly detection

AbstractThis paper addresses the cybersecurity of hierarchical control of AC microgrids with distributed secondary control. The false data injection (FDI) cyberattack is assumed to alter the operating frequency of inverter‐based distributed generators (DGs) in an islanded microgrid. For the microgrids consisting of the grid‐forming inverters with the secondary control operating in a distributed manner, the attack on one DG deteriorates not only the corresponding DG but also the other DGs that receive the corrupted information via the distributed communication network. To this end, an FDI attack detection algorithm based on a combination of Gaussian process regression and one‐class support vector machine (OC‐SVM) anomaly detection is introduced. This algorithm is unsupervised in the sense that it does not require labelled abnormal data for training which is difficult to collect. The Gaussian process model predicts the response of the DG, and its prediction error and estimated variances provide input to an OC‐SVM anomaly detector. This algorithm returns enhanced detection performance than the standalone OC‐SVM. The proposed cyberattack detector is trained and tested with the data collected from a 4 DG microgrid test model and is validated in both simulation and hardware‐in‐the‐loop testbeds.

Mutual Trust Evaluation Model in Quantum Distributed Communication Network

Mutual Trust Evaluation Model in Quantum Distributed Communication Network

High-precision time–frequency synchronization in Ethernet communication networks

Currently, many applications require not only high-speed communication, but also high-precision time–frequency synchronization. However, the simultaneous transmission and exchange of digital time signals with continuous analog frequency signals face challenges in large-scale Ethernet networks. Here we report a method to converge high-precision time–frequency synchronization and communication in fiber-optic distributed Ethernet networks. The time–frequency signals are multiplexed by wavelength division to the communication data with different optical wavelengths. The transmission error of the time–frequency signals was measured and controlled using a round-trip method. Network switching is implemented by add-drop multiplexing with a continuous wavelength selector switch and IP packets for the frequency signal and communication data, respectively. Experimental verification is carried out in a five-node network. In this network, high-precision time–frequency synchronization in Ethernet communication networks is realized. The frequency instability is less than 3 × 10−14@1 s, and the time synchronization offset is less than 70 ps with the communication rate of 1.25 Gbps. These results demonstrate the broad application prospects of the proposed method in distributed coherent detection fields with multiple nodes.

Resilient AC Microgrids Against Correlated Attacks

Multi-inverter AC microgrids increasingly rely on local embedded controllers and distributed communication networks to meet operational requirements, which makes the microgrids vulnerable to physical and cyber attacks. Conventional resilient control strategies generally assume that the attack signals are bounded and uncorrelated. In this paper, we study the ramifications of allowing the antagonistic inputs to be unbounded and correlated. We consider a two-layer hierarchy for networked multi-agent systems with two opposing teams on different directed communication graphs: a control protagonist team with cooperative multi-inverter microgrids and an attack antagonist team with interacting attackers. We consider three types of unbounded attack injections launched from the antagonist layer, namely, coordinated and correlated attacks on the sensor measurements, as well as generally unbounded attacks on actuator commands and communication channels. We propose a fully distributed control framework to guarantee uniform ultimate boundedness for the secondary frequency regulation and voltage containment of AC microgrids against malicious attacks. The proposed results are validated on a modified IEEE 34-bus test feeder system, which is emulated in a controller/hardware-in-the-loop environment.

Predefined-time formation control of the quadrotor-UAV cluster’ position system

Based on the state observer and sliding-mode control technique, this paper analytically addresses the fully distributed predefined-time formation problem of quadrotor-UAV cluster’ position system. Firstly, the dynamic model of the quadrotor-UAV is established, and the formation problem of quadrotor-UAV cluster is transformed into the consensus problem of second-order multi-agent systems. Subsequently, considering the distributed communication network, a distributed predefined-time state observer is designed for each quadrotor-UAV to estimate the relative state information between the virtual leader quadrotor-UAV and itself. On this basis, a distributed formation controller is designed via the sliding mode control method, so that all quadrotor-UAVs in the cluster can quickly achieve the desired relative position relationship with the leader within a predetermined time, that is, form the desired formation shape, and maintain it precisely. Finally, some simulation experiments of the three-dimensional motion of the formation control of quadrotor-UAV cluster are carried out. The simulation results demonstrate the effectiveness and practicality of the proposed predefined-time cooperative formation control strategy.

Decentralized Adaptive Fault-Tolerant Cooperative Control for Multiple UAVs with Input Saturation and State Constraints

This paper proposes a fault-tolerant cooperative control (FTCC) scheme for multiple UAVs in a distributed communication network against input saturation, full-state constraints, actuator faults, and unknown dynamics. Firstly, by considering physical limitations, an auxiliary control signal is designed to simplify the analysis process. Secondly, to avoid the difficulty in the back-stepping design caused by full-state constraints, virtual control signals are constructed to transform constrained variables, while the dynamic surface control is adopted to avoid the phenomenon of “differential explosion.” Thirdly, a disturbance observer (DO) is designed to estimate the unknown uncertainty caused by parameter uncertainty and actuator fault. Moreover, a recurrent wavelet fuzzy neural network (RWFNN) is used to compensate for the estimation errors of DO. Finally, it is proved that all states are uniformly ultimately bounded (UUB) by Lyapunov and invariant set theory. The effectiveness of the proposed scheme is further demonstrated by the simulation results.

Distributed Fractional-Order Intelligent Adaptive Fault-Tolerant Formation-Containment Control of Two-Layer Networked Unmanned Airships for Safe Observation of a Smart City.

This article investigates a distributed fractional-order fault-tolerant formation-containment control (FOFTFCC) scheme for networked unmanned airships (UAs) to achieve safe observation of a smart city. In the proposed control method, an interval type-2 fuzzy neural network (IT2FNN) is first developed for each UA to approximate the unknown term associated with the loss-of-effectiveness faults in the distributed error dynamics, and then a disturbance observer (DO) is proposed to compensate for the approximation error and bias fault encountered by each UA, such that the composite learning strategy composed of the IT2FNN and the DO is obtained for each UA. Moreover, fractional-order (FO) calculus is incorporated into the control scheme to provide an extra degree of freedom for the parameter adjustments. The salient feature of the proposed control scheme is that the composite learning algorithm and FO calculus are integrated to achieve a satisfactory fault-tolerant formation-containment control performance even when a portion of leader/follower UAs is subjected to the actuator faults in a distributed communication network. Furthermore, it is shown by Lyapunov stability analysis that all leader UAs can track the virtual leader UA with time-varying offset vectors, and all follower UAs can converge into the convex hull spanned by the leader UAs. Finally, comparative hardware-in-the-loop (HIL) experimental results are presented to show the effectiveness and superiority of the proposed method.

A distributed VSG control method for a battery energy storage system with a cascaded H-bridge in a grid-connected mode

With the high penetration of renewable energy, new challenges, such as power fluctuation suppression and inertial support capability, have arisen in the power sector. Battery energy storage systems play an essential role in renewable energy integration. In this paper, a distributed virtual synchronous generator (VSG) control method for a battery energy storage system (BESS) with a cascaded H-bridge converter in a grid-connected mode is proposed. The VSG is developed without communication dependence, and state-of-charge (SOC) balancing control is achieved using the distributed average algorithm. Owing to the low varying speed of SOC, the bandwidth of the distributed communication networks is extremely slow, which decreases the cost. Therefore, the proposed method can simultaneously provide inertial support and accurate SOC balancing. The stability is also proved using root locus analysis. Finally, simulations under different conditions are carried out to verify the effectiveness of the proposed method.

A hierarchical control scheme with bi-level communication networks for the interconnected DC microgrids cluster

This paper proposes a hierarchical control scheme based on distributed alternating direction method of multipliers (ADMM) for the interconnected DC microgrids cluster. This scheme is fully distributed and consists of three coordinated control levels through bi-level distributed communication networks. The constraints of the economic dispatch problem are decoupled for tertiary control and decomposed into global and local optimization problems using the ADMM framework. To realize parallel computing of ADMM, we designed a fast second-order distributed average consensus algorithm to estimate the global average exchanged value and the average output power of a single microgrid, thus removing the coupling within the microgrids cluster and individual microgrid. Considering the voltage deviation caused by the traditional droop control, voltage control without droop based on the distributed algorithm is proposed to realize voltage restoration and power distribution among dispatchable generators for individual microgrid, combined with traditional primary and secondary control. Numerical simulations of an off-grid microgrids cluster are designed to validate the effectiveness of the proposed control method. Results show that the proposed hierarchical control scheme can ensure the independence and privacy of information, which is beneficial to solve technical and economic challenges brought by centralized optimization. In addition, the proposed algorithm can be applied to more realistic scenarios, such as conditions for time-varying loads and cost functions.

Communication MWSN Data Transmission Mechanism Based on a Wireless Sensor Network

A wireless sensor network (WSN) is considered to have a wide application prospect in a smart distribution network due to its unique low power consumption, fast self-organization, and superior coordination. In order to study the communication MWSN data transmission mechanism based on a wireless sensor network, the difficulty of applying a wireless sensor network to an intelligent distribution network communication is to meet the real-time and reliability requirements of power industry distribution network data communication specifications under the limited network resources. Firstly, the transmission probability is calculated by considering the path loss, the speed of the node, the direction of the node, and the residual energy of the node. In addition, the transmission and replacement order of the message is determined according to the survival time of the data message. Simulation experiments are carried out on MATLAB. The result shows the proposed routing algorithm, compared with the DT algorithm, flooding algorithm, and FAD algorithm, the delivery rate of data messages is increased by at least 6%, and the number of copies is reduced by at least 10% compared with the flooding algorithm and the FAD algorithm. On the basis of satisfying the mobility of sink nodes, the algorithm proposed in this paper can effectively save network energy consumption and prolong the network lifetime.

Microgrid’s multi-VSGs cooperative control method based on distributed communication

Microgrid is a group of interconnected Distributed Generators (DGs) and loads. As an application of renewable energy, microgrid began to enter people’s vision. The technology of Virtual Synchronous Generator (VSG) is becoming a research hotspot owing to it can supply inertia for distributed generation system. For the oscillation problem in microgrid after using VSG technology, this paper theoretically analyzes the variation in output frequency and power of parallel VSGs after system load fluctuation, and proposes a cooperative control method of multi-VSGs. Through distributed communication network, this method improves the output control of each VSG. In the transient process, applied method alleviates the uneven distribution in the active power of the microgrid, while reducing the difference in the output frequency of each VSG, therefore, it can significantly restrain the oscillation and ameliorate the dynamic characteristics of microgrid. A simulation model is established to prove the validity and advantage of this method at the end of this paper.