Adaptive Online Disturbance Location Considering Anisotropy of Frequency Propagation Speeds

Online disturbance location is of significant importance to wide-area response-based situational awareness, stability analysis and control of the power systems. This paper presents an adaptive online disturbance location method that considers the anisotropy of the frequency propagation speed (FPS). The method geographically partitions the studied power system into several different regions offline. It then extracts the FPS vector for events occurring in each region. The method uses one event simulation for each region. The online simulations take into account changes in the generation, load and power grid topology. The location of the disturbance in each region is estimated specifically based on the speed vector of the region. Parallel computing is used to accelerate the estimation procedure. A part of the North and Central China interconnected power grid, which covers 7 provinces, is selected as an example to verify the effectiveness of the proposed disturbance location method and to analyze the speedup properties of the parallel computing.

[1]  Ying-Hong Lin,et al.  An adaptive PMU based fault detection/location technique for transmission lines. I. Theory and algorithms , 2000 .

[2]  L. Fickert,et al.  Automated power system event detector and classifier , 2004, 2004 11th International Conference on Harmonics and Quality of Power (IEEE Cat. No.04EX951).

[3]  Yutian Liu,et al.  Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm , 2008 .

[4]  Kyung Soo Kook,et al.  Analysis of Power System Disturbances Based on Wide-Area Frequency Measurements , 2007, 2007 IEEE Power Engineering Society General Meeting.

[5]  W. L. Peterson,et al.  Adaptive estimation of power system frequency deviation and its rate of change for calculating sudden power system overloads , 1990 .

[6]  Tianshu Bi,et al.  Study on power system disturbance identification and location based on WAMS , 2012, PES 2012.

[7]  Y. Liu,et al.  Generator Trip Identification Using Wide-Area Measurements and Historical Data Analysis , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[8]  Joe H. Chow,et al.  Power system disturbance identification from recorded dynamic data at the Northfield substation , 2003 .

[9]  A.G. Phadke,et al.  Power system frequency monitoring network (FNET) implementation , 2005, IEEE Transactions on Power Systems.

[10]  V. Terzija,et al.  Adaptive underfrequency load shedding based on the magnitude of the disturbance estimation , 2006, IEEE Transactions on Power Systems.

[11]  Tianshu Bi,et al.  A novel hybrid state estimator for including synchronized phasor measurements , 2008 .

[12]  G. Radman,et al.  Wide area frequency based generation trip event location estimation , 2012, 2012 IEEE Power and Energy Society General Meeting.

[13]  Chul-Hwan Kim,et al.  New settings-free fault location algorithm based on synchronised sampling , 2011 .

[14]  Boon-Teck Ooi,et al.  Frequency deviation of thermal power plants due to wind farms , 2006, IEEE Transactions on Energy Conversion.

[15]  Tao Xia,et al.  Wide-area Frequency Based Event Location Estimation , 2007, 2007 IEEE Power Engineering Society General Meeting.

[16]  Shantanu Padmanabhan,et al.  Smart Overhead Lines Autoreclosure Algorithm Based on Detailed Fault Analysis , 2013, IEEE Transactions on Smart Grid.

[17]  Ruisheng Diao,et al.  The characteristic ellipsoid methodology and its application in power systems , 2014, T&D 2014.

[18]  S. M. Rovnyak,et al.  Dynamic event detection and location using wide area phasor measurements , 2011 .

[19]  Kyung Soo Kook,et al.  Measurement and Simulation of Wide-area Frequency in US Eastern Interconnected Power System , 2013 .

[20]  S Arianos,et al.  Power grid vulnerability: a complex network approach. , 2008, Chaos.

[21]  Vladimir Terzija,et al.  Adaptive underfrequency load shedding integrated with a frequency estimation numerical algorithm , 2002 .

[22]  John P. Snyder,et al.  Map Projections: A Working Manual , 2012 .

[23]  Vahid Madani,et al.  Wide-Area Monitoring, Protection, and Control of Future Electric Power Networks , 2011, Proceedings of the IEEE.

[24]  James S. Thorp,et al.  Continuum modeling of electromechanical dynamics in large-scale power systems , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[25]  J. S. Thorp,et al.  The effect of electromechanical wave controllers on inter-area modes , 2012, 2012 IEEE Power and Energy Society General Meeting.

[26]  M. Wancerz,et al.  Power system stability enhancement by WAMS-based supplementary control of multi-terminal HVDC networks , 2013 .

[27]  S.M. Rovnyak,et al.  Clustering-Based Dynamic Event Location Using Wide-Area Phasor Measurements , 2008, IEEE Transactions on Power Systems.

[28]  Di Wu,et al.  Extended Topological Metrics for the Analysis of Power Grid Vulnerability , 2012, IEEE Systems Journal.

[29]  W.A. Mittelstadt,et al.  Use of the WECC WAMS in Wide-Area Probing Tests for Validation of System Performance and Modeling , 2009, IEEE Transactions on Power Systems.