The Characteristic Ellipsoid Methodology and its Application in Power Systems

The characteristic ellipsoid (CELL) method to monitor dynamic behaviors of a power system is proposed. Multi-dimensional minimum-volume-enclosing characteristic ellipsoids are built using synchronized phasor measurements. System dynamic behaviors are identified by tracking the change rate of the CELL's characteristic indices. Decision tree techniques are used to link the CELL's characteristic indices and the system's dynamic behaviors and to determine types, locations and related information about the dynamic behaviors. The knowledge base of representative transient events is created by offline simulations based on the full Western Electric Coordinating Council (WECC) model. Two case studies demonstrate that the CELL method combined with the decision trees can detect transient events and their features with good accuracy.

[1]  Real-time monitoring as enabler for smart transmission grids , 2011, 2011 IEEE Power and Energy Society General Meeting.

[2]  Y. V. Makarov,et al.  Building decision trees for characteristic ellipsoid method to monitor power system transient behaviors , 2010, IEEE PES General Meeting.

[3]  Peng Sun,et al.  Computation of Minimum Volume Covering Ellipsoids , 2002, Oper. Res..

[4]  Piyush Kumar,et al.  Minimum-Volume Enclosing Ellipsoids and Core Sets , 2005 .

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

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

[7]  Kenneth E. Martin,et al.  WACS-Wide-Area Stability and Voltage Control System: R&D and Online Demonstration , 2005, Proceedings of the IEEE.

[8]  Leonid Khachiyan,et al.  Rounding of Polytopes in the Real Number Model of Computation , 1996, Math. Oper. Res..

[9]  Igor Rivin.  Surface area and other measures of ellipsoids , 2007, Adv. Appl. Math..

[10]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

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

[12]  Zhenyu Huang,et al.  Automatic implementation of Prony analysis for electromechanical mode identification from phasor measurements , 2010, IEEE PES General Meeting.

[13]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[14]  D. Titterington,et al.  Minimum Covering Ellipses , 1980 .

[15]  Vijay Vittal,et al.  An Online Dynamic Security Assessment Scheme Using Phasor Measurements and Decision Trees , 2007 .

[16]  Michael J. Todd,et al.  On Khachiyan's algorithm for the computation of minimum-volume enclosing ellipsoids , 2007, Discret. Appl. Math..

[17]  Ying-Hong Lin,et al.  An adaptive PMU based fault detection/location technique for transmission lines. II. PMU implementation and performance evaluation , 2000 .

[18]  Luis Mateus Rocha,et al.  Singular value decomposition and principal component analysis , 2003 .

[19]  C. W. Taylor.  WACS-wide-area stability and voltage control system: R&D and on-line demonstration , 2004 .

[20]  S. Corsi,et al.  A Real-Time Voltage Instability Identification Algorithm Based on Local Phasor Measurements , 2008, IEEE Transactions on Power Systems.

[21]  Jian Ma,et al.  Characteristic ellipsoid method for monitoring power system dynamic behavior using phasor measurements , 2007, 2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability.