Distributed Estimation and Secondary Control of Autonomous Microgrid

Zhang, Guoyue; Li, Chaoyong; Qi, Donglian; Xin, Huanhai

doi:10.1109/tpwrs.2016.2590431

Cited by 33 publications

(15 citation statements)

References 26 publications

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“…Moreover, this method is accountable to coordinate MG with the utility before executing interconnection, ie, any changeover from autonomous to grid tied. Stage 2 control calculates frequency and amplitude parameters in the MG ( ω g , A g ), and it relates to initial set points ω * and A * and direct errors Δ ω and Δ A to all networks to reestablish output voltage . The phase between the MG and the main national grid is assessed and transport to all subsystems to coordinate with the MG phase.…”

Section: Hierarchical Architecture Of Microgridmentioning

confidence: 99%

“…Stage 2 control calculates frequency and amplitude parameters in the MG (ω g , A g ), and it relates to initial set points ω * and A * and direct errors Δω and ΔA to all networks to reestablish output voltage. [41][42][43][44][45][46] The phase between the MG and the main national grid is assessed and transport to all subsystems to coordinate with the MG phase. Thus, this level control is utilized in MGs to attain realizable output through the linkage proven among various DG nodes.…”

Section: Secondary/supplementary Controller (Voltage and Frequencymentioning

confidence: 99%

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Control of distributed generation systems for microgrid applications: A technological review

Arfeen

Khairuddin

Larik

et al. 2019

Int Trans Electr Energ Syst

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Summary Microgrid implementation requires effective and efficient strategies for controlling the grid parameters. Various problems are encountered with the deployment of distributed generation in terms of reverse power, an imbalance between power generation and nonlinear load. This paper is focused on the existing controllers in terms of their merits and limitations. Furthermore, the state of the art of the local power distribution system especially on renewable energy resources along with energy storage methods is explored. Reliability and stability of power flow between sources and consumers via voltage source inverter are also considered. The emerging microgrid concept in islanding and grid‐connected mode using different controller along with soft computing algorithm are presented. This paper gives the reader fast insight into the context of the controller and its application.

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Section: Hierarchical Architecture Of Microgridmentioning

confidence: 99%

Section: Secondary/supplementary Controller (Voltage and Frequencymentioning

confidence: 99%

Control of distributed generation systems for microgrid applications: A technological review

Arfeen

Khairuddin

Larik

et al. 2019

Int Trans Electr Energ Syst

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“…calculate elements of weight matrix involved in Agent i and its neighbors using Metropolis rule 8: collect the initial value of neighbors 9: t = t + 1 move to Iteration 1 10: while t < t 0 do t 0 is the number of iteration needed to reach the consensus state 11:…”

Section: Algorithmmentioning

confidence: 99%

Multi-Agent System with Plug and Play Feature for Distributed Secondary Control in Microgrid—Controller and Power Hardware-in-the-Loop Implementation

et al. 2018

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Distributed control and optimization strategies are a promising alternative approach to centralized control within microgrids. In this paper, a multi-agent system is developed to deal with the distributed secondary control of islanded microgrids. Two main challenges are identified in the coordination of a microgrid: (i) interoperability among equipment from different vendors; and (ii) online re-configuration of the network in the case of alteration of topology. To cope with these challenges, the agents are designed to communicate with physical devices via the industrial standard IEC 61850 and incorporate a plug and play feature. This allows interoperability within a microgrid at agent layer as well as allows for online re-configuration upon topology alteration. A test case of distributed frequency control of islanded microgrid with various scenarios was conducted to validate the operation of proposed approach under controller and power hardware-in-the-loop environment, comprising prototypical hardware agent systems and realistic communications network.

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“…Since its inception, distributed control of a multiagent network instantly became a popular topic in the community, witnessed by its broad applications in diverse areas, such as formation control, smart grid, and artificial intelligence, to name a few. As is well known, one common goal in these venues is to ensure that all networked systems reach an agreement/consensus with only local interactions in the presence of intermittent communications .…”

Section: Introductionmentioning

confidence: 99%

Dynamic average consensus with topology balancing under a directed graph

Xin

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary In this paper, the distributed average tracking problem is studied on the premise of a strongly connected directed graph. To this end, we propose a weight balance strategy that could potentially make the adjacency matrix doubly stochastic for any strongly connected directed graph. The proposed scheme is fully distributive with finite time convergence and we again prove that network connectivity (described by the first left eigenvector) is instrumental in networked control systems. Then, a discrete‐time average tracking observer is introduced to ensure that all networked systems can track the average of the reference signals with bounded error. Simulation results verify the effectiveness of the proposed methods.

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Distributed Estimation and Secondary Control of Autonomous Microgrid

Cited by 33 publications

References 26 publications

Control of distributed generation systems for microgrid applications: A technological review

Control of distributed generation systems for microgrid applications: A technological review

Multi-Agent System with Plug and Play Feature for Distributed Secondary Control in Microgrid—Controller and Power Hardware-in-the-Loop Implementation

Dynamic average consensus with topology balancing under a directed graph

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