Algorithm for screening PMU data for power system events

Wide Area Monitoring Systems (WAMS) are a Smart Grid technology which can enhance real-time situational awareness for power sytem operators. WAMS are created from a network of synchronized measurement devices taking voltage and current measurements from the power system. This network is also known as a synchrophasor network. A synchrophasor network was created at the University of Texas at Austin to obtain real power system measurements for power system analysis. The network is continuously operating and recording voltage phasor data (voltage magnitude, angle, and frequency) at a rate of 30 data points per second. Because of the high volume of PMU data generated it is difficult to detect and analyze power system events of interest. To help power system operators more easily monitor the power system and easily detect events in PMU data, two different types of methods were created to automatically screen the data for events. The first method screens a small window of PMU voltage data and detects events based on a variety of techniques. The second method is an off-line method that detects events based on the magnitude of low frequency oscillations in PMU voltage data.

[1]  Yilu Liu,et al.  Off-line event filter for the wide area frequency measurements , 2006, 2006 IEEE Power Engineering Society General Meeting.

[2]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[3]  Math Bollen,et al.  Time-frequency and time-scale domain analysis of voltage disturbances , 2000 .

[4]  I. Gu,et al.  The use of time-varying AR models for the characterization of voltage disturbances , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[5]  Kjetil Uhlen,et al.  Estimation of Eastern Denmark's electromechanical modes from ambient phasor measurement data , 2010, IEEE PES General Meeting.

[6]  Hun Choi,et al.  A Filter Bank and a Self-Tuning Adaptive Filter for the Harmonic and Interharmonic Estimation in Power Signals , 2012, IEEE Transactions on Instrumentation and Measurement.

[7]  J. Quintero,et al.  Oscillation monitoring system based on wide area synchrophasors in power systems , 2007, 2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability.

[8]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[9]  Edward J. Powers,et al.  New robust voltage sag disturbance detector using an adaptive prediction error filter , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[10]  P. Vaidyanathan.  Multirate Systems And Filter Banks , 1992 .

[11]  Joseph Euzebe Tate,et al.  Event detection and visualization based on phasor measurement units for improved situational awareness , 2008 .

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

[13]  Marco Invernizzi,et al.  Perturbation identification via voltage phasor monitoring in transmission systems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[14]  Edward J. Powers,et al.  Characterization of distribution power quality events with Fourier and wavelet transforms , 2000 .

[15]  Ning Zhou,et al.  Performance of Three Mode-Meter Block-Processing Algorithms for Automated Dynamic Stability Assessment , 2008, IEEE Transactions on Power Systems.