Changing Communication Topology Research Articles

Fully distributed control to coordinate charging efficiencies for energy storage systems

This study proposes a novel fully distributed coordination control (DCC) strategy to coordinate charging efficiencies of energy storage systems (ESSs). To realize this fully DCC strategy in an active distribution system (ADS) with high penetration of intermittent renewable generation, a two-layer consensus algorithm is proposed and applied. It collects global information in the first layer and achieves pinning-based DCC in the second layer. Basic objectives of the proposed DCC for ESSs are: ① to coordinate the ESSs and improve efficiency using associated marginal charging costs (MCCs) in a fully distributed manner; ② to reduce local power mismatch and power transmission losses; ③ to adapt to unintentional communication topology changes. The effectiveness and adaptability of the proposed DCC approach are both validated by simulation results.

Distributed reinforcement learning to coordinate current sharing and voltage restoration for islanded DC microgrid

A novel distributed reinforcement learning (DRL) strategy is proposed in this study to coordinate current sharing and voltage restoration in an islanded DC microgrid. Firstly, a reward function considering both equal proportional current sharing and cooperative voltage restoration is defined for each local agent. The global reward of the whole DC microgrid which is the sum of the local rewards is regarged as the optimization objective for DRL. Secondly, by using the distributed consensus method, the predefined pinning consensus value that will maximize the global reward is obtained. An adaptive updating method is proposed to ensure stability of the above pinning consensus method under uncertain communication. Finally, the proposed DRL is implemented along with the synchronization seeking process of the pinning reward, to maximize the global reward and achieve an optimal solution for a DC microgrid. Simulation studies with a typical DC microgrid demonstrate that the proposed DRL is computationally efficient and able to provide an optimal solution even when the communication topology changes.

A distributed secondary scheme with terminal sliding mode controller for energy storages in an islanded microgrid

Different levels of the stored energy is the main challenge in control of the distributed energy storages (DESs) in an islanded microgrid. The conventional power-droop and the secondary distributed controllers of distributed generators (DGs) does not consider the long time-span dynamics of state of charge (SoC) of the DESs. Subsequently, the DESs with lower initial SoCs are discharged before other DESs. Besides, the SoC-droop primary control results in further deviation of the frequency of the microgrid. In this paper, the secondary control scheme is employed to share the power mismatch, match the SoCs of the DESs, and regulate the frequency and voltage of the microgrid. The proposed scheme has distributed cooperative architecture, and employs distributed terminal sliding mode controller (DTSMC) for the state regulators, and proportional controller for distributed power-sharing and SoC-matching. The proposed scheme organizes the controllable DGs, the DESs with limited SoCs, and the uncooperative DGs with unknown generation, through communicating between the neighbor cooperative DGs and DESs. Performance of the designed DTSMC is verified for the changes of communication topology, time-delays, data drop-outs, load variations, and external disturbances. DTSMC provides finite-time convergence, fast transients, and improved robustness.

Distributed Multi-Agent System-Based Load Frequency Control for Multi-Area Power System in Smart Grid

This paper presents an intelligent controller for “load frequency control (LFC)” application in “smart grid (SG)”environment having changes in communication topology (CT) via a multi-agent system (MAS) technology. In this study, network-induced effects, time delay, and change in CT have been addressed to examine the system performance in a closed loop. An event-triggered control method is used to reduce the communication burden in a network. An intelligent controller based on reinforcement learning consists of two levels, estimator agent and controller agent, in each multi-area system. Particle swarm optimization is used to tune the controller parameters. Furthermore, the proposed control strategy and system architecture as MAS for LFC in SG are analyzed in detail, verified for various load conditions and different network configurations. In addition, mean-square error of the power system states with CT is also analyzed. The results of this study validate the feasibility of the proposed control, as well as the capability of the MAS for the operation of LFC in SG with changes in CT.

Multiagent-Based Distributed State of Charge Balancing Control for Distributed Energy Storage Units in AC Microgrids

In this paper, a multiagent-based distributed control algorithm has been proposed to achieve state of charge (SoC) balance of distributed energy storage (DES) units in an ac microgrid. The proposal uses frequency scheduling instead of adaptive droop gain to regulate the active power. Each DES unit is taken as an agent and it schedules its own frequency reference given of the real power droop controller according to the SoC values of all other DES units. Further, to obtain the average SoC value of DES, the dynamic average consensus algorithm is utilized by each agent. A generalized small-signal model of the proposed frequency scheduling for the proposed frequency scheduling is developed in order to verify the stability of the control system and to guide control parameters design. The convergence characteristics for the dynamic consensus adopted in the multiagent system are also analyzed to choose the proper control parameter. Experimental results verified the effectiveness, the robustness against communication topology changes, and capability of “plug & play” for the proposed multiagent system through different case studies.

Distributed Coordination Load Shedding of Islanded Microgrids Based on Sub-Gradient Algorithm

This paper proposes a novel distributed coordination load shedding (DCLS) approach for an islanded microgrid (MG) using sub-gradient algorithm of multi-agent system. The main objective is to achieve practical and optimal LS and obtain an optimum amount of load to be shed in a fully distributed manner under large disturbances. To coordinate the controllable loads (CLs) in an MG, an LS level (LSL) is first defined and evaluated locally to take the CL capacity and the LS willingness into consideration. Then, by updating the LSLs and the local frequency deviation measured based on frequency-inertia dynamics response, the proposed DCLS can be accomplished based on the sub-gradient algorithm. More importantly, only local information is needed to be updated during the entire DCLS process. Hence, the power supply demand balance can be well maintained, the utilization of LS can be significantly improved, and the requirements for communication topology changes can be adaptively met in a fully distributed way. The simulation results indicate that the proposed sub-gradient-based distributed coordination algorithm and corresponding DCLS are effective and adaptive.

A Novel Distributed Secondary Coordination Control Approach for Islanded Microgrids

This paper develops a new distributed secondary cooperative control scheme to coordinate distributed generators (DGs) in islanded microgrids (MGs). A finite time frequency regulation strategy containing a consensus-based distributed active power regulator is presented, which can not only guarantee the active power sharing but also enable all DGs' frequencies to converge to the reference value within a finite time. This enables the frequency and voltage control designs to be separated. Then an observer-based distributed voltage regulator involving certain reactive power sharing constraints is proposed, which allows different set points for different DGs and, thus, accounts for the line impedance effects. The steady-state performance analysis shows that the voltage regulator can accurately address the issue of global voltage regulation and accurate reactive power sharing. Moreover, all the distributed controllers are equipped with bounded control inputs to suppress the transient overshoot, and they are implemented through sparse communication networks. The effectiveness of the control in case of load variation, plug-and-play capability, communication topology change, link failure, time delays, and data drop-out are verified by the simulation of an islanded MG in MATLAB/SimPowerSystems.

Load Frequency Control with Communication Topology Changes in Smart Grid

In this paper, the quality of service of communication infrastructure implemented in multiarea power system for load frequency control application is assessed in smart grid environment. In this study, network induced effects time delay, packet loss, bandwidth, quantization, and change in communication topology (CT) has been addressed to examine the system performance in closed loop. Uncertainty in time delay is approximated using deterministic and stochastic models. The network is modeled considering different network configurations, i.e., change in CT. The modeled communication network guarantees control relevant properties, i.e., stability. The decentralized controller and linear matrix inequality-based linear quadratic regulator is implemented to reduce the dynamic performance (mean square error of states variables) of power system as CT changes. Simulation results suggest that the proposed scheme of networked control is superior with respect to other design methods available in the literature, and thus robust to communication imperfections.

Target tracking using multiple unmanned aerial vehicles: Graph theoretic nonlinear control approach

This paper develops a cooperative controller for multiple Unmanned Aerial Vehicles (UAVs) with application to target tracking. The cooperation between the UAVs is established based on an algebraic graph connection and the target information is provided externally by pinning it into a subset of the network. A backstepping-like technique is employed to design a consensus-based controller for each UAV in order to achieve target tracking in 3-D. The proposed controller computes commanded signals for the speed, flight path angle, and heading angle to track the target. The paper considers both the cases of fixed and dynamically changing communication topologies. It is shown that target tracking is achieved for fixed connection topology, if the graph has a directed spanning tree; and for the dynamically changing topology, if the union of the graphs over finite time intervals has a directed spanning tree. The system’s stability is shown using a Lyapunov function-based approach for these cases. All tracking errors are shown to be bounded as long as the target states and its derivatives up to second order are bounded. Detailed numerical simulations further illustrate the controller performance.

Event‐triggered H ∞ Markovian switching pinning control for group consensus of large‐scale systems

This study investigates the pinning control issue for group consensus of large-scale systems (LSSs) with communication topology changes and network disturbances. A node selection principle is firstly designed to choose appropriate control nodes, and a centralised pinning control protocol is proposed to drive LSSs achieving group consensus. For reducing control command updates, an event-trigger is employed to determine whether control signal need be changed. Since the node receives compound exogenous disturbances from its communication neighbours, an improved point-to-area H ∞ index is advocated as a system robustness performance. Considering communication topology changes, an event-triggered H ∞ Markovian switching pinning control law is finally established and a sufficient controller and event-trigger co-design method is presented. Under this strategy, the group consensus of LSSs with a pre-scheduled H ∞ performance can be simultaneously achieved while the Markovian switching topologies are tolerated, moreover, only fewer nodes are controlled and seldom control commands are updated. Finally, illustrative examples are given to demonstrate the effectiveness of the proposed theoretical result.

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Guest Editorial

Multiperiod Scheduling for Wireless Sensor Networks: A Distributed Consensus Approach

In wireless sensor networks, many sensors face energy constraints and can switch among different work modes to save energy. How to properly schedule work modes is important for network utility maximization (NUM) in the long run. This paper proposes multiperiod scheduling to maximize total network utility by considering energy constraints and periodic sensing requirements. This NUM problem presents challenging mixed-integer programming, and it is difficult to solve by using a centralized approach under complete information. Thus, we first simplify the multiperiod problem to an equivalent single-period problem, and then further reduce it to a pure-integer programming problem, which can be solved easily in a centralized way. As for the cases without a centralized coordinator among all sensors, we propose an average consensus-based distributed algorithm (ACDA) to distributively schedule the work modes of all sensors using only local information. We prove that ACDA converges exponentially fast and reaches global optimum as long as the energy consumption of running the algorithm is ignorable. The proposed distributed solution is also robust against packet drop, node failures, and the changes of communication topology. Extensive simulation results have also shown the effectiveness of the proposed distributed algorithms.

Synchronising second-order multi-agent systems under dynamic topology via reference model-based algorithm

This paper revisits the synchronisation problem for second-order multi-agent systems (MASs) under dynamically changing communication topology. By employing the reference model-based synchronisation algorithm, it is finally shown that synchronisation for both the position and velocity states can be achieved if the union of the communication topologies has a directed spanning tree frequently enough. This extends the existing results obtained for second-order MASs which exploits mild communication topology condition guaranteeing the synchronisation to a more general case. Convergence analysis is successfully performed by exploiting the product properties of row-stochastic matrices, which can also provide us with an estimate the convergence rate towards the synchronisation.

Swarming behaviors in multi-agent systems with nonlinear dynamics

The dynamic analysis of a continuous-time multi-agent swarm model with nonlinear profiles is investigated in this paper. It is shown that, under mild conditions, all agents in a swarm can reach cohesion within a finite time, where the upper bounds of the cohesion are derived in terms of the parameters of the swarm model. The results are then generalized by considering stochastic noise and switching between nonlinear profiles. Furthermore, swarm models with limited sensing range inducing changing communication topologies and unbounded repulsive interactions between agents are studied by switching system and nonsmooth analysis. Here, the sensing range of each agent is limited and the possibility of collision among nearby agents is high. Finally, simulation results are presented to demonstrate the validity of the theoretical analysis.

Modeling and distributed gain scheduling strategy for load frequency control in smart grids with communication topology changes

In this paper, we investigate the modeling and distributed control problems for the load frequency control (LFC) in a smart grid. In contrast with existing works, we consider more practical and real scenarios, where the communication topology of the smart grid changes because of either link failures or packet losses. These topology changes are modeled as a time-varying communication topology matrix. By using this matrix, a new closed-loop power system model is proposed to integrate the communication topology changes into the dynamics of a physical power system. The globally asymptotical stability of this closed-loop power system is analyzed. A distributed gain scheduling LFC strategy is proposed to compensate for the potential degradation of dynamic performance (mean square errors of state vectors) of the power system under communication topology changes. In comparison to conventional centralized control approaches, the proposed method can improve the robustness of the smart grid to the variation of the communication network as well as to reduce computation load. Simulation results show that the proposed distributed gain scheduling approach is capable to improve the robustness of the smart grid to communication topology changes.

A leader election algorithm for dynamic networks with causal clocks

An algorithm for electing a leader in an asynchronous network with dynamically changing communication topology is presented. The algorithm ensures that, no matter what pattern of topology changes occurs, if topology changes cease, then eventually every connected component contains a unique leader. The algorithm combines ideas from the Temporally Ordered Routing Algorithm for mobile ad hoc networks (Park and Corson in Proceedings of the 16th IEEE Conference on Computer Communications (INFOCOM), pp. 1405–1413 (1997) with a wave algorithm (Tel in Introduction to distributed algorithms, 2nd edn. Cambridge University Press, Cambridge, MA, 2000), all within the framework of a height-based mechanism for reversing the logical direction of communication topology links (Gafni and Bertsekas in IEEE Trans Commun C–29(1), 11–18 1981). Moreover, a generic representation of time is used, which can be implemented using totally-ordered values that preserve the causality of events, such as logical clocks and perfect clocks. A correctness proof for the algorithm is provided, and it is ensured that in certain well-behaved situations, a new leader is not elected unnecessarily, that is, the algorithm satisfies a stability condition.

Stationary Consensus of Asynchronous Discrete-Time Second-Order Multi-Agent Systems Under Switching Topology

This paper is concerned with the asynchronous consensus problem of discrete-time second-order multi-agent system under dynamically changing communication topology, in which the asynchrony means that each agent detects the neighbors' state information to update its state information by its own clock. It is not assumed that the agents' clocks are synchronized. Nor is it assumed that the time sequence over which each agent update its state information is evenly spaced. By using tools from graph theory and nonnegative matrix theory, particularly the product properties of row-stochastic matrices from an infinite set, we finally show that essentially the same result as that for the synchronous discrete-time system holds in the face of asynchronous setting. This generalizes the existing result to a very general case.

A Sufficient Condition for Convergence of Sampled-Data Consensus for Double-Integrator Dynamics With Nonuniform and Time-Varying Communication Delays

This technical note investigates a discrete-time second-order consensus algorithm for networks of agents with nonuniform and time-varying communication delays under dynamically changing communication topologies in a sampled-data setting. Some new proof techniques are proposed to perform the convergence analysis. It is finally shown that under certain assumptions upon the velocity damping gain and the sampling period, consensus is achieved for arbitrary bounded time-varying communication delays if the union of the associated digraphs of the interaction matrices in the presence of delays has a directed spanning tree frequently enough.

Consensus strategy for a class of multi‐agents with discrete second‐order dynamics

SUMMARYThis paper investigates consensus strategies for a group of agents with discrete second‐order dynamics under directed communication topology. Consensus analysis for both the fixed topology and time‐varying topology cases is systematically performed by employing a novel graph theoretic methodology as well as the classical nonnegative matrix theory. Furthermore, it is shown that the necessary and sufficient condition for the agents under fixed communication topology to reach consensus is that the communication topology has a spanning tree; and sufficient conditions for the agents to reach consensus when allowing for the dynamically changing communication topologies are also given. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed results. Copyright © 2011 John Wiley & Sons, Ltd.

Longitudinal Vehicle Guidance in Networks with changing Communication Topology

This paper presents a novel approach to control layout for vehicle guidance with changing communication topology. The control adapts to possible communication failure and varying access to other vehicle's information including stability conditions for arbitrary switching between different topologies. On the one hand the control layout addresses deficiencies in vehicle-to-vehicle communication, it further accounts for the reduction of spacing errors and a limitation in velocities and accelerations of following vehicles to avoid saturations. All objectives are formulated as one set of linear matrix inequalities that are solved for each of the adaptive vehicle controls. The optimization is presented for different information topologies of a platoon with four vehicles and the effectiveness of preserving the control performance in case of communication failure is shown through simulations.