Graph Computing based Distributed State Estimation with PMUs

Power system state estimation plays a fundamental and critical role in the energy management system (EMS). To achieve a high performance and accurate system states estimation, a graph computing based distributed state estimation approach is proposed in this paper. Firstly, a power system network is divided into multiple areas. Reference buses are selected with PMUs being installed at these buses for each area. Then, the system network is converted into multiple independent areas. In this way, the power system state estimation could be conducted in parallel for each area and the estimated system states are obtained without compromise of accuracy. IEEE 118-bus system and MP 10790-bus system are employed to verify the results accuracy and present the promising computation performance.

[1]  Fangxing Li,et al.  GPU-Based Fast Decoupled Power Flow With Preconditioned Iterative Solver and Inexact Newton Method , 2017, IEEE Transactions on Power Systems.

[2]  Xi Chen,et al.  A High-Performance Energy Management System Based on Evolving Graph , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  Garng M. Huang,et al.  CIM/E Oriented Graph Database Model Architecture and Parallel Network Topology Processing , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[4]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

[5]  Zhiwei Wang,et al.  Simplify Power Flow Calculation Using Terminal Circuit and PMU Measurements , 2019, 2019 IEEE Power & Energy Society General Meeting (PESGM).

[6]  S. Pekarek,et al.  Multiobjective Optimization of Multiconductor DC Power Cables , 2019, 2019 IEEE Electric Ship Technologies Symposium (ESTS).

[7]  Yachen Tang,et al.  Extraction of Energy Information From Analog Meters Using Image Processing , 2015, IEEE Transactions on Smart Grid.

[8]  C. Brice,et al.  Multiprocessor Static State Estimation , 1982, IEEE Transactions on Power Apparatus and Systems.

[9]  Xi Chen,et al.  Exploration of Graph Computing in Power System State Estimation , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[10]  George N Korres,et al.  A Distributed Multiarea State Estimation , 2011, IEEE Transactions on Power Systems.

[11]  Antonio J. Conejo,et al.  Electric Energy Systems : Analysis and Operation , 2008 .

[12]  Zhiwei Wang,et al.  Graph Based Power Flow Calculation for Energy Management System , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[13]  Zhiwei Wang,et al.  Graph Computing-Based WLS Fast Decoupled State Estimation , 2020, IEEE Transactions on Smart Grid.

[14]  Ali Abur,et al.  Parallel state estimation using multiprocessors , 1990 .

[15]  Chen Yuan,et al.  Economic sizing of distributed energy resources for reliable community microgrids , 2017, 2017 IEEE Power & Energy Society General Meeting.

[16]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[17]  H. Poor,et al.  Fully Distributed State Estimation for Wide-Area Monitoring Systems , 2012, IEEE Transactions on Smart Grid.

[18]  Yi Lu,et al.  Exploration of Bi-Level PageRank Algorithm for Power Flow Analysis Using Graph Database , 2018, 2018 IEEE International Congress on Big Data (BigData Congress).

[19]  Jiankang Wang,et al.  An integrated algorithm for evaluating plug-in electric vehicle’s impact on the state of power grid assets , 2019 .

[20]  Zhiwei Wang,et al.  Optimization of Battery Energy Storage to Improve Power System Oscillation Damping , 2018, IEEE Transactions on Sustainable Energy.

[21]  Joseph H. Eto,et al.  Computational Needs for the Next Generation Electric Grid , 2011 .

[22]  Walid Saad,et al.  Stochastic Games for Power Grid Protection Against Coordinated Cyber-Physical Attacks , 2018, IEEE Transactions on Smart Grid.