Signal processing for TFR of synchro-phasor data

With increased number of phasor measurement units (PMUs) deployment in the power system, there is growing interest in real-time event detection. The study in this paper presents post-processing approach to map the event occurrence for large sets of synchro-phasor data available from different regions/buses in the power network. The time–frequency representation (TFR) is applied to analyse the transients that contain rapid variations in amplitude or phase during the event occurrence against post-event conditions. This is based on reassigned smoothed-pseudo-Wigner–Ville distribution. The capability to correctly localise TF regions, where signals are locally coupled, is assessed on synthetic signal and PMUs signals. Important information such as time marginals is estimated to determine any event present in the analysed signal segment. Furthermore, the event localisation is enhanced applying Hough transform on TFR with calculation of group delay and reassignment vector. The developed procedure gives reasonable results for highlighting the distinct representation of events in study. The proposed approach can be easily implemented in real-time monitoring for event detection in the power system.

[1]  Patrick Flandrin,et al.  Improving the readability of time-frequency and time-scale representations by the reassignment method , 1995, IEEE Trans. Signal Process..

[2]  Lefteri H. Tsoukalas,et al.  PMU data characterization and application to stability monitoring , 2006 .

[3]  J. A. Hollman,et al.  Real-Time Network Simulation with PC-Cluster , 2002, IEEE Power Engineering Review.

[4]  D. Lauria,et al.  On Hilbert transform methods for low frequency oscillations detection , 2014 .

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

[6]  William J. Williams,et al.  Improved time-frequency representation of multicomponent signals using exponential kernels , 1989, IEEE Trans. Acoust. Speech Signal Process..

[7]  Mike Brookes,et al.  A Quantitative Assessment of Group Delay Methods for Identifying Glottal Closures in Voiced Speech , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  A.G. Phadke,et al.  An Alternative for Including Phasor Measurements in State Estimators , 2006, IEEE Transactions on Power Systems.

[9]  Fethi Bereksi-Reguig,et al.  Detection of the valvular split within the second heart sound using the reassigned smoothed pseudo Wigner–Ville distribution , 2013, BioMedical Engineering OnLine.

[10]  Pablo Laguna,et al.  Characterization of Dynamic Interactions Between Cardiovascular Signals by Time-Frequency Coherence , 2012, IEEE Transactions on Biomedical Engineering.

[11]  Lucio Soibelman,et al.  A time-frequency approach for event detection in non-intrusive load monitoring , 2011, Defense + Commercial Sensing.

[12]  Masashi Sugiyama,et al.  Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation , 2009, SDM.

[13]  J. O. Gjerde,et al.  Preprocessing synchronized phasor measurement data for spectral analysis of electromechanical oscillations in the Nordic Grid , 2015 .

[14]  Abhisek Ukil,et al.  Abrupt Change Detection in Power System Fault Analysis using Adaptive Whitening Filter and Wavelet Transform , 2015, ArXiv.

[15]  G. N. Taranto,et al.  A comparative case study of online voltage instability monitoring , 2015, 2015 IEEE Eindhoven PowerTech.

[16]  Ines M. Cecilio,et al.  Multivariate Detection of Transient Disturbances for Uni- and Multirate Systems , 2015, IEEE Transactions on Control Systems Technology.

[17]  Chun-Lien Su,et al.  Visualization of Large-Scale Power System Operations Using Phasor Measurements , 2006, 2006 International Conference on Power System Technology.

[18]  T. W. Cease,et al.  Synchronized phasor measurements of a power system event , 1994 .

[19]  Fushuan Wen,et al.  Application of wide area measurement systems to islanding detection of bulk power systems , 2013, IEEE Transactions on Power Systems.

[20]  Peng Yang,et al.  Power System State Estimation Using PMUs With Imperfect Synchronization , 2013, IEEE Transactions on Power Systems.

[21]  Bhavik R. Bakshi,et al.  Representation of process trends—III. Multiscale extraction of trends from process data , 1994 .

[22]  C. Russell,et al.  Use of the Wigner‐Ville distribution in interpreting and identifying ULF waves in triaxial magnetic records , 2008 .

[23]  F. Bereksi-Reguig,et al.  SMOOTHED-PSEUDO WIGNER–VILLE DISTRIBUTION OF NORMAL AND AORTIC STENOSIS HEART SOUNDS , 2005 .

[24]  G.T. Heydt,et al.  A Distributed State Estimator Utilizing Synchronized Phasor Measurements , 2007, IEEE Transactions on Power Systems.