Distributed Data Traffic Scheduling With Awareness of Dynamics State in Cyber Physical Systems With Application in Smart Grid

Qu, Chunxiao; Chen, Wei; Song, Ju Bin; Li, Husheng

doi:10.1109/tsg.2015.2399247

Cited by 31 publications

(17 citation statements)

References 16 publications

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“…The selection procedure continues until one of the gateways is selected as the best neighbor. Other backpressure based scheduling strategies have also been proposed in literature [12], but they do not consider the fact that data rates in SG might be too low to effectively develop a backpressure gradient among neighboring nodes. This paper addresses these limitations and proposes a Mixed Integer Linear Program (MILP) based static routing and link scheduling strategy.…”

Section: Introductionmentioning

confidence: 99%

A Routing and Link Scheduling Strategy for Smart Grid NAN Communications

Biswas

Centeno

2019

2019 North American Power Symposium (NAPS)

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As large scale deployment of smart devices in the power grid continues, research efforts need to increasingly focus on efficient communication of generated information. This paper describes a strategy for static routing and scheduling of messages in a multi-hop wireless Smart Grid Neighborhood Area Network (NAN) with multiple source nodes and a common set of destinations or gateways. The problem is formulated as a Mixed Integer Linear Program (MILP) and solved using commercial optimization solver CPLEX. Feasibility of the scheme is demonstrated using different network models, constraints, message injection rates, and initial conditions. It is shown that the proposed approach can be used to generate an optimal link schedule for collecting user-generated bids in a transactive energy market in the least possible time. It is also shown that the methodology is applicable to multiple destination nodes and that their location affects message delivery time.

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Section: Introductionmentioning

confidence: 99%

A Routing and Link Scheduling Strategy for Smart Grid NAN Communications

Biswas

Centeno

2019

2019 North American Power Symposium (NAPS)

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“…One critical challenge is the transient and dynamic issues of DES and cyber resources allocation. Most existing research focused on the characteristics of steady state [19]. They ignored the transient and dynamic issues in control systems [20].…”

Section: Introductionmentioning

confidence: 99%

A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid

Sun

Zhu

2019

Applied Sciences

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With the rapid growth of distributed energy sources, power grid has become a flexible and complex networked control system. However, it increases the chances of being a denial-of-service attack, which degrades the performance of the power grid, even causing cascading failures. To mitigate negative effects from denial-of-service attack and enhance the reliability of the power grid, we propose a networked control system structure based optimization scheme that is derived from a Stackelberg game model for the frequency regulation of a power grid with distributed energy sources. In the proposed game model, both denial-of-service attacker and control system designer as a defender are considered without using any analytical model. For defenders, we propose a sparse neural network based DES control and system structure design scheme. The neural network is used to approximate the desired control output and reinforce signals for the improvements of short- and long-term performance. It also introduces the sparse regulation of column grouping in the neural network learning process to explore the structure of control system that involves the placement of sensor, distributed energy sources actuator, and communication topology. For denial-of-service attackers, the related attack constraints and attack rewards are established. The solution of game equilibrium is considered as an optimal solution for both denial-of-service attack strategy and control structure. An offline optimization algorithm is proposed to solve the game equilibrium. The effectiveness of proposed scheme is verified by two cases, which illustrate the optimal solutions of both control structure and denial-of-service attack strategy.

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“…Note that we have proposed virtual‐queue‐based back‐pressure scheduling in Ref. , where a two‐hop network model is considered. In that paper, it was assumed that the transmission and reception are error‐free and no packet loss problem exists, so only the impact of communication delays on the distributed scheduling was studied.…”

Section: Introductionmentioning

confidence: 99%

Traffic scheduling in distributed networked control systems with packet losses and delays for power systems voltage stabilization

et al. 2016

IEEJ Transactions Elec Engng

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In this paper, medium access control (MAC) sublayer data traffic scheduling in distributed networked control systems (DNCSs) with networked induced packet losses and delays is investigated for stabilization and control of system dynamics. An effectiveinformation-directed distributed sensor selection algorithm is proposed to select sensors to participate in delay-tolerant information filtering for state estimation. The distributed scheduling algorithm is based on the total amount of effective information for all measurements stored in the sensors. We consider packet loss in wireless networks as a special type of delay, so the impact of packet losses on effective information amount can also be studied for the formulation of distributed scheduling strategy in the presence of packet losses. The distributed data traffic scheduling considering packet losses and delays is carried out in a networked system with data packet dropout governed by a Markov process for voltage regulation and stabilization with distributed energy resources (DERs). Experimental results demonstrate that the scheduling algorithm with awareness of the system dynamic state can well control the power system dynamics.

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Distributed Data Traffic Scheduling With Awareness of Dynamics State in Cyber Physical Systems With Application in Smart Grid

Cited by 31 publications

References 16 publications

A Routing and Link Scheduling Strategy for Smart Grid NAN Communications

A Routing and Link Scheduling Strategy for Smart Grid NAN Communications

A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid

Traffic scheduling in distributed networked control systems with packet losses and delays for power systems voltage stabilization

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