Event-Trigger Extended Kalman Filter Design for Distributed Generation Network with Limited Communication
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- 英文论文题名：Event-Trigger Extended Kalman Filter Design for Distributed Generation Network with Limited Communication
- 论文作者：Xing Xin ; Luyu Li ; Sen Li ; Zhen Li ; Jian Li ; Zhen Chen ; Xiangdong Liu
- 英文论文作者：Xing Xin ; Luyu Li ; Sen Li ; Zhen Li ; Jian Li ; Zhen Chen ; Xiangdong Liu ; Beijing Institute of Electronic System Engineering ; School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology
- 年：2017
- 作者机构：Beijing Institute of Electronic System Engineering;School of Automation & Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology;
- 英文论文关键词：Event-trigger extended Kalman filter(ET-EKF) ; real-time estimation ; distributed generation network ; smart grids ; phasor measurement unit(PMU)
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TM76;TN713
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：570k
- 原文格式：D
- 会议级别：全国
摘要
For distributed generation(DG) network, it is important to estimate the real-time states. The information-centric networking(ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm, which reduces the communication burden in ICN, is to drop the observation of each smart grid in a random way. However, the accuracy of this algorithm recession decays rapidly with the increasing drop rate. To guarantee an appropriate estimation accuracy when the drop rate increases, this paper introduces the event-trigger strategy into the estimation algorithm. An event-trigger extended kalman filter(ET-EKF) is established in this paper to adapt the nonlinearity of DG system. ET-EKF reduces the communication burden and achieves an appropriate estimation accuracy at the same time. Finally, its feasibility and performance are demonstrated using the standard IEEE 39-bus system with phasor measurement units(PMUs).
For distributed generation(DG) network, it is important to estimate the real-time states. The information-centric networking(ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm, which reduces the communication burden in ICN, is to drop the observation of each smart grid in a random way. However, the accuracy of this algorithm recession decays rapidly with the increasing drop rate. To guarantee an appropriate estimation accuracy when the drop rate increases, this paper introduces the event-trigger strategy into the estimation algorithm. An event-trigger extended kalman filter(ET-EKF) is established in this paper to adapt the nonlinearity of DG system. ET-EKF reduces the communication burden and achieves an appropriate estimation accuracy at the same time. Finally, its feasibility and performance are demonstrated using the standard IEEE 39-bus system with phasor measurement units(PMUs).
For distributed generation(DG) network, it is important to estimate the real-time states. The information-centric networking(ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm, which reduces the communication burden in ICN, is to drop the observation of each smart grid in a random way. However, the accuracy of this algorithm recession decays rapidly with the increasing drop rate. To guarantee an appropriate estimation accuracy when the drop rate increases, this paper introduces the event-trigger strategy into the estimation algorithm. An event-trigger extended kalman filter(ET-EKF) is established in this paper to adapt the nonlinearity of DG system. ET-EKF reduces the communication burden and achieves an appropriate estimation accuracy at the same time. Finally, its feasibility and performance are demonstrated using the standard IEEE 39-bus system with phasor measurement units(PMUs).
引文
[1]V.C.Gungor,D.Sahin,T.Kocak,S.Ergut,C.Buccella,C.Cecati,and G.P.Hancke,“Smart grid technologies:communication technologies and standards,”IEEE Trans.Ind.Inform.,vol.7,no.4,pp.529-539,2011.
[2]M.Erol-Kantarci and H.T.Mouftah,“Energy-efficient information and communication infrastructures in the smart grid:A survey on interactions and open issues,”IEEE Commun.Surveys Tuts.,vol.17,no.1,pp.179-197,2015.
[3]C.Chen,K.Nagananda,G.Xiong,S.Kishore,and L.V.Snyder,“A communication-based appliance scheduling scheme for consumer-premise energy management systems,”IEEETrans.Smart Grid,vol.4,no.1,pp.56-65,2013.
[4]L.Xie,D.-H.Choi,S.Kar,and H.V.Poor,“Fully distributed state estimation for wide-area monitoring systems,”IEEE Trans.Smart Grid,vol.3,no.3,pp.1154-1169,2012.
[5]J.Liu,J.Tang,F.Ponci,A.Monti,C.Muscas,and P.A.Pegoraro,“Trade-offs in pmu deployment for state estimation in active distribution grids,”IEEE Trans.Smart Grid,vol.3,no.2,pp.915-924,2012.
[6]Y.Ge,A.J.Flueck,D.-K.Kim,J.-B.Ahn,J.-D.Lee,and D.-Y.Kwon,“Power system real-time event detection and associated data archival reduction based on synchrophasors,”IEEETrans.Smart Grid,vol.6,no.4,pp.2088-2097,2015.
[7]K.V.Katsaros,W.K.Chai,N.Wang,G.Pavlou,H.Bontius,and M.Paolone,“Information-centric networking for machine-to-machine data delivery:a case study in smart grid applications,”IEEE Network,vol.28,no.3,pp.58-64,2014.
[8]E.Ghahremani and I.Kamwa,“Dynamic state estimation in power system by applying the extended kalman filter with unknown inputs to phasor measurements,”IEEE Trans.Power Syst.,vol.26,no.4,pp.2556-2566,Nov 2011.
[9]A.K.Singh and B.C.Pal,“Decentralized dynamic state estimation in power systems using unscented transformation,”IEEE Trans.Power Syst.,vol.29,no.2,pp.794-804,March2014.
[10]K.Emami,T.Fernando,H.H.C.Iu,H.Trinh,and K.P.Wong,“Particle filter approach to dynamic state estimation of generators in power systems,”IEEE Trans.Power Syst.,vol.30,no.5,pp.2665-2675,Sept 2015.
[11]J.B.Leite and J.R.S.Mantovani,“Distribution system state estimation using the hamiltonian cycle theory,”IEEE Trans.Smart Grid,vol.7,no.1,pp.366-375,2016.
[12]Y.C.Chen,J.Wang,A.D.Dom?nguez-Garc?a,and P.W.Sauer,“Measurement-based estimation of the power flow jacobian matrix,”IEEE Trans.Smart Grid,vol.7,no.5,pp.2507-2515,Sept 2016.
[13]B.Sinopoli,L.Schenato,M.Franceschetti,K.Poolla,M.Jordan,S.S.Sastry et al.,“Kalman filtering with intermittent observations,”IEEE Trans.Autom.Control,vol.49,no.9,pp.1453-1464,2004.
[14]Y.Ho and C.Cassandras,“A new approach to the analysis of discrete event dynamic systems,”Automatica,vol.19,no.2,pp.149-167,1983.
[15]K.J.?Astr?m and B.Bernhardsson,“Comparison of periodic and event based sampling for first-order stochastic systems,”in 14th IFAC world congress,1999.
[16]J.Wu,Q.-S.Jia,K.H.Johansson,and L.Shi,“Event-based sensor data scheduling:Trade-off between communication rate and estimation quality,”IEEE Trans.Autom.Control,vol.58,no.4,pp.1041-1046,2013.
[17]D.Shi,T.Chen,and L.Shi,“Event-triggered maximum likelihood state estimation,”Automatica,vol.50,no.1,pp.247-254,2014.
[18]J.Wu,K.H.Johansson,and L.Shi,“A stochastic online sensor scheduler for remote state estimation with time-out condition,”IEEE Trans.Autom.Control,vol.59,no.11,pp.3110-3116,2014.
[19]H.Sun,Z.Wang,J.Wang,Z.Huang,N.Carrington,and J.Liao,“Data-driven power outage detection by social sensors,”IEEE Trans.Smart Grid,vol.7,no.5,pp.2516-2524,Sept 2016.
[20]X.Liu,L.Li,L.Zhen,T.Fernando,and H.H.C.Iu,“Stochastic stability condition for the extended kalman filter with intermittent observations,”IEEE Trans.Circuits Syst.II,Exp.Briefs,2016,doi:10.1109/TCSII.2016.2578956.
[2]M.Erol-Kantarci and H.T.Mouftah,“Energy-efficient information and communication infrastructures in the smart grid:A survey on interactions and open issues,”IEEE Commun.Surveys Tuts.,vol.17,no.1,pp.179-197,2015.
[3]C.Chen,K.Nagananda,G.Xiong,S.Kishore,and L.V.Snyder,“A communication-based appliance scheduling scheme for consumer-premise energy management systems,”IEEETrans.Smart Grid,vol.4,no.1,pp.56-65,2013.
[4]L.Xie,D.-H.Choi,S.Kar,and H.V.Poor,“Fully distributed state estimation for wide-area monitoring systems,”IEEE Trans.Smart Grid,vol.3,no.3,pp.1154-1169,2012.
[5]J.Liu,J.Tang,F.Ponci,A.Monti,C.Muscas,and P.A.Pegoraro,“Trade-offs in pmu deployment for state estimation in active distribution grids,”IEEE Trans.Smart Grid,vol.3,no.2,pp.915-924,2012.
[6]Y.Ge,A.J.Flueck,D.-K.Kim,J.-B.Ahn,J.-D.Lee,and D.-Y.Kwon,“Power system real-time event detection and associated data archival reduction based on synchrophasors,”IEEETrans.Smart Grid,vol.6,no.4,pp.2088-2097,2015.
[7]K.V.Katsaros,W.K.Chai,N.Wang,G.Pavlou,H.Bontius,and M.Paolone,“Information-centric networking for machine-to-machine data delivery:a case study in smart grid applications,”IEEE Network,vol.28,no.3,pp.58-64,2014.
[8]E.Ghahremani and I.Kamwa,“Dynamic state estimation in power system by applying the extended kalman filter with unknown inputs to phasor measurements,”IEEE Trans.Power Syst.,vol.26,no.4,pp.2556-2566,Nov 2011.
[9]A.K.Singh and B.C.Pal,“Decentralized dynamic state estimation in power systems using unscented transformation,”IEEE Trans.Power Syst.,vol.29,no.2,pp.794-804,March2014.
[10]K.Emami,T.Fernando,H.H.C.Iu,H.Trinh,and K.P.Wong,“Particle filter approach to dynamic state estimation of generators in power systems,”IEEE Trans.Power Syst.,vol.30,no.5,pp.2665-2675,Sept 2015.
[11]J.B.Leite and J.R.S.Mantovani,“Distribution system state estimation using the hamiltonian cycle theory,”IEEE Trans.Smart Grid,vol.7,no.1,pp.366-375,2016.
[12]Y.C.Chen,J.Wang,A.D.Dom?nguez-Garc?a,and P.W.Sauer,“Measurement-based estimation of the power flow jacobian matrix,”IEEE Trans.Smart Grid,vol.7,no.5,pp.2507-2515,Sept 2016.
[13]B.Sinopoli,L.Schenato,M.Franceschetti,K.Poolla,M.Jordan,S.S.Sastry et al.,“Kalman filtering with intermittent observations,”IEEE Trans.Autom.Control,vol.49,no.9,pp.1453-1464,2004.
[14]Y.Ho and C.Cassandras,“A new approach to the analysis of discrete event dynamic systems,”Automatica,vol.19,no.2,pp.149-167,1983.
[15]K.J.?Astr?m and B.Bernhardsson,“Comparison of periodic and event based sampling for first-order stochastic systems,”in 14th IFAC world congress,1999.
[16]J.Wu,Q.-S.Jia,K.H.Johansson,and L.Shi,“Event-based sensor data scheduling:Trade-off between communication rate and estimation quality,”IEEE Trans.Autom.Control,vol.58,no.4,pp.1041-1046,2013.
[17]D.Shi,T.Chen,and L.Shi,“Event-triggered maximum likelihood state estimation,”Automatica,vol.50,no.1,pp.247-254,2014.
[18]J.Wu,K.H.Johansson,and L.Shi,“A stochastic online sensor scheduler for remote state estimation with time-out condition,”IEEE Trans.Autom.Control,vol.59,no.11,pp.3110-3116,2014.
[19]H.Sun,Z.Wang,J.Wang,Z.Huang,N.Carrington,and J.Liao,“Data-driven power outage detection by social sensors,”IEEE Trans.Smart Grid,vol.7,no.5,pp.2516-2524,Sept 2016.
[20]X.Liu,L.Li,L.Zhen,T.Fernando,and H.H.C.Iu,“Stochastic stability condition for the extended kalman filter with intermittent observations,”IEEE Trans.Circuits Syst.II,Exp.Briefs,2016,doi:10.1109/TCSII.2016.2578956.