Multicast Routing for Decentralized Control of Cyber Physical Systems with an Application in Smart Grid

Li, Husheng; Lai, Lifeng; Poor, H. Vincent

doi:10.1109/jsac.2012.120708

Cited by 160 publications

(91 citation statements)

References 15 publications

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“…Considering typical parameter in [10,11], the simulation result is presented in Figure 2. Figure 2 shows the PCC voltage deviation at bus 1 and its estimation result.…”

Section: Simulation Resultsmentioning

confidence: 99%

Internet of Things for Smart Grid Automation

Rana¹

2017

IRATJ

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“…Considering typical parameter in [10,11], the simulation result is presented in Figure 2. Figure 2 shows the PCC voltage deviation at bus 1 and its estimation result.…”

Section: Simulation Resultsmentioning

confidence: 99%

Internet of Things for Smart Grid Automation

Rana¹

2017

IRATJ

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“…As their power generation pattern are generally intermittent in nature, so it needs to monitor the PCC voltages and keep it at a reference value by applying a suitable control technique [31]. Generally speaking, the microgrid may also be install in the remote and mountain areas, so its monitoring and stability should be managed in a distributed way.…”

Section: A Distribution Power System Incorporating Microgridmentioning

confidence: 99%

“…Based on the standard specifications of the IEEE 4-bus system [31], the admittance matrix is given in (44). Now we can convert the transfer function into the following form [31]:ẋ…”

Section: A Distribution Power System Incorporating Microgridmentioning

confidence: 99%

Distributed dynamic state estimation over a lossy communication network with an application to smart grids

Rana

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-In contrast to the traditional centralised power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modelled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system (EMS) through a lossy communication network where measurements are lost. This can seriously deteriorate the system monitoring performance and even lose network stability. Secondly, as the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. Availability of the system states provides designers an accurate picture of the power network, so a suitable control strategy can be applied to avoid massive blackouts due to lose network stability. Particularly, the proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.Index Terms-Distributed controller, distributed dynamic estimation, energy management system, packet losses, linear matrix inequality.

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“…Wireless sensing and control facilitate the design of mobile systems, enabling closed-loop control of mobile devices, such as automated guided vehicles, mobile robots, and unmanned aerial vehicles. CPS are becoming a promising technology for a wide range of application domains, such as smart building [3], intelligent transportation [7], power grid [6], and industrial control [4].…”

Section: Introductionmentioning

confidence: 99%

Collaborative State Estimation and Actuator Scheduling for Cyber-Physical Systems Under Random Multiple Events

Kritikakou

Cao

2018

Ad-Hoc, Mobile, and Wireless Networks

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Abstract. The design of fast and effective coordination among sensors and actuators in Cyber-Physical Systems (CPS) is a fundamental, but challenging issue, especially when the system model is a priori unknown and multiple random events can simultaneously occur. We propose a novel collaborative state estimation and actuator scheduling algorithm with two phases. In the first phase, we propose a Gaussian Mixture Model (GMM)-based method using the random event physical field distribution to estimate the locations and the states of events. In the second phase, based on the number of identified events and the number of available actuators, we study two actuator scheduling scenarios and formulate them as Integer Linear Programming (ILP) problems with the objective to minimize the actuation delay. We validate and demonstrate the performance of the proposed scheme through both simulations and physical experiments for a home temperature control application.

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Multicast Routing for Decentralized Control of Cyber Physical Systems with an Application in Smart Grid

Cited by 160 publications

References 15 publications

Internet of Things for Smart Grid Automation

Internet of Things for Smart Grid Automation

Distributed dynamic state estimation over a lossy communication network with an application to smart grids

Collaborative State Estimation and Actuator Scheduling for Cyber-Physical Systems Under Random Multiple Events

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