Distributed Energy Resources Topology Identification via Graphical Modeling

Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the grid more than ever before, which introduces tremendous changes in the distribution grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainab...

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Vydáno v:	IEEE transactions on power systems Ročník 32; číslo 4; s. 2682 - 2694
Hlavní autoři:	Yang Weng, Yizheng Liao, Rajagopal, Ram
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.07.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Computer simulation Density estimation robust algorithm Distributed energy resources Distributed generation distribution grids Electric potential Electric power distribution Electric utilities Electricity distribution Electronic devices Energy resources Energy sources Gas generators Generators graphical model Graphical models Modelling mutual information Network topology Power flow Probabilistic logic Probabilistic methods Probability theory Sensors Topology topology identification Voltage measurement Wind power generation
ISSN:	0885-8950, 1558-0679
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Abstract	Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the grid more than ever before, which introduces tremendous changes in the distribution grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution grid. But the exact distribution system topology is difficult to obtain due to frequent distribution grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution grid topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.
AbstractList	Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the grid more than ever before, which introduces tremendous changes in the distribution grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution grid. But the exact distribution system topology is difficult to obtain due to frequent distribution grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution grid topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.
Author	Yizheng Liao Rajagopal, Ram Yang Weng
Author_xml	– sequence: 1 surname: Yang Weng fullname: Yang Weng email: yangweng@stanford.edu organization: Stanford Univ., Stanford, CA, USA – sequence: 2 surname: Yizheng Liao fullname: Yizheng Liao email: yzliao@stanford.edu organization: Stanford Univ., Stanford, CA, USA – sequence: 3 givenname: Ram surname: Rajagopal fullname: Rajagopal, Ram email: ramr@stanford.edu organization: Stanford Univ., Stanford, CA, USA
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SubjectTerms	Computer simulation Density estimation robust algorithm Distributed energy resources Distributed generation distribution grids Electric potential Electric power distribution Electric utilities Electricity distribution Electronic devices Energy resources Energy sources Gas generators Generators graphical model Graphical models Modelling mutual information Network topology Power flow Probabilistic logic Probabilistic methods Probability theory Sensors Topology topology identification Voltage measurement Wind power generation
Title	Distributed Energy Resources Topology Identification via Graphical Modeling
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