Wide-area multiple line-outages detection in power complex networks

Abstract Multiple line outages (MLOs) are common in blackouts, the detection of MLOs are very important for the robusty and securety of power grid. Many insightful methods have been reported to handle with line outage detection, but few of them focus on the detection of MLOs that happen in a short period of time. To deal with this promblem, based on the complex network theory, some novel algorithms are developed by using phasor measurement units (PMUs) information. By invoking virtual adaptive observers, the presented algorithms monitor the connectivity status between buses, which make the algorithms tolerant to the interaction effects between the multiple line outages. Besides, the proposed algorithms also address the reconstruction of the real-time adjacency matrix of the power transmission networks. Simulation results demonstrate the effectiveness of the presented algorithms.

[1]  Lamine Mili,et al.  A robust estimation method for topology error identification , 1999 .

[2]  Z. Sumic,et al.  Fuzzy set theory based outage determination [for distribution networks] , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[3]  Ali Abur,et al.  External system line outage identification using phasor measurement units , 2013, IEEE Transactions on Power Systems.

[4]  Hao Zhu,et al.  Sparse Overcomplete Representations for Efficient Identification of Power Line Outages , 2012, IEEE Transactions on Power Systems.

[5]  I. Dobson,et al.  Estimating the Propagation and Extent of Cascading Line Outages From Utility Data With a Branching Process , 2012, IEEE Transactions on Power Systems.

[6]  T.J. Overbye,et al.  Line Outage Detection Using Phasor Angle Measurements , 2008, IEEE Transactions on Power Systems.

[7]  Daniel S. Kirschen,et al.  Criticality in a cascading failure blackout model , 2006 .

[8]  Antonio Gómez Expósito,et al.  Reduced Substation Models for Generalized State Estimation , 2001 .

[9]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[10]  Réka Albert,et al.  Structural vulnerability of the North American power grid. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Newton G. Bretas,et al.  Synchronism versus stability in power systems , 1999 .

[12]  Qi Huang,et al.  A novel approach for fault location of overhead transmission line with noncontact magnetic-field measurement , 2013, 2013 IEEE Power & Energy Society General Meeting.

[13]  N. N. Schulz,et al.  Knowldege-Based System for Distribution System Outage Locating Using Comprehensive Information , 2002, IEEE Power Engineering Review.

[14]  A. Vargas,et al.  Inference of operative configuration of distribution networks using fuzzy logic techniques - Part II: extended real-time model , 2005, IEEE Transactions on Power Systems.

[15]  Murti V. Salapaka,et al.  On the Problem of Reconstructing an Unknown Topology via Locality Properties of the Wiener Filter , 2010, IEEE Transactions on Automatic Control.

[16]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[17]  Edgar H. Bristol,et al.  Pattern recognition: An alternative to parameter identification in adaptive control , 1977, Autom..

[18]  Ali Abur,et al.  Identifying the unknown circuit breaker statuses in power networks , 1995 .

[19]  Venugopal V. Veeravalli,et al.  Quickest Line Outage Detection and Identification , 2016, IEEE Transactions on Power Systems.

[20]  Ljupco Kocarev,et al.  Estimating topology of networks. , 2006, Physical review letters.

[21]  A. Monticelli.  Modeling circuit breakers in weighted least squares state estimation , 1993 .

[22]  Zhong-Ping Jiang,et al.  Topology identification of complex dynamical networks. , 2010, Chaos.

[23]  Bin Deng,et al.  Topology identification of uncertain nonlinearly coupled complex networks with delays based on anticipatory synchronization. , 2013, Chaos.

[24]  L.A. Kojovic,et al.  Summary of Distributed Resources Impact on Power Delivery Systems , 2008, IEEE Transactions on Power Delivery.

[25]  Florian Dörfler,et al.  Synchronization and transient stability in power networks and non-uniform Kuramoto oscillators , 2009, Proceedings of the 2010 American Control Conference.

[26]  Michael Chertkov,et al.  Synchronization in complex oscillator networks and smart grids , 2012, Proceedings of the National Academy of Sciences.

[27]  K. A. Clements,et al.  Topology error identification using normalized Lagrange multipliers , 1998 .

[28]  José M. F. Moura,et al.  Modeling of Future Cyber–Physical Energy Systems for Distributed Sensing and Control , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[29]  Hui Ren,et al.  Long-Term Effect of the n-1 Criterion on Cascading Line Outages in an Evolving Power Transmission Grid , 2008, IEEE Transactions on Power Systems.

[30]  Anil Pahwa,et al.  A computer assisted intelligent storm outage evaluator for power distribution systems , 1990 .

[31]  G. Besançon.  Remarks on nonlinear adaptive observer design , 2000 .

[32]  Ricard Sol'e,et al.  Analysis of major failures in Europe’s power grid , 2009, 0906.1109.

[33]  H. Vincent Poor,et al.  Outage detection in power distribution networks with optimally-deployed power flow sensors , 2013, 2013 IEEE Power & Energy Society General Meeting.

[34]  Y. Lai,et al.  Optimizing controllability of complex networks by minimum structural perturbations. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  M. Pai.  Energy function analysis for power system stability , 1989 .

[36]  Yue Zhao,et al.  On PMU location selection for line outage detection in wide-area transmission networks , 2012, 2012 IEEE Power and Energy Society General Meeting.

[37]  Jobst Heitzig,et al.  How dead ends undermine power grid stability , 2014, Nature Communications.