Synchronized ambient data-based extraction of interarea modes using Hankel block-enhanced DMD

Abstract Ambient data-driven algorithms for the extraction of electromechanical modes serve as useful and practical methods for assessing the small signal stability of power systems in real time. This paper proposes a Hankel block-enhanced dynamic mode decomposition (HeDMD) based approach for the online extraction of modes from synchrophasor ambient data. To improve the ability of the dynamic mode decomposition (DMD) algorithm to capture modes from ambient data, a Hankel matrix is introduced to rearrange the measured data. With the extension function of the Hankel block, the mode frequency and damping ratio can be estimated by using only a short data window, which can effectively reduce the computation time. The performance of the proposed HeDMD method for the extraction of modes is investigated using simulated data from both the IEEE 4-generator system and the IEEE 16-generator system and by using ambient measurement data from a real power system.

[1]  P. Kundur,et al.  Power system stability and control , 1994 .

[2]  Vaithianathan Venkatasubramanian,et al.  Fast Frequency-Domain Decomposition for Ambient Oscillation Monitoring , 2015, IEEE Transactions on Power Delivery.

[3]  H. Ghasemi,et al.  Oscillatory stability limit prediction using stochastic subspace identification , 2006, IEEE Transactions on Power Systems.

[4]  Siu-Kui Au,et al.  Bayesian Approach in the Modal Analysis of Electromechanical Oscillations , 2017, IEEE Transactions on Power Systems.

[5]  Vaithianathan Mani Venkatasubramanian,et al.  Oscillation monitoring from ambient PMU measurements by Frequency Domain Decomposition , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[6]  Daniel J. Trudnowski,et al.  Initial results in electromechanical mode identification from ambient data , 1997 .

[7]  Igor Mezic,et al.  Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator , 2016, SIAM J. Appl. Dyn. Syst..

[8]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[9]  James S. Thorp,et al.  Synchronized Phasor Measurement Applications in Power Systems , 2010, IEEE Transactions on Smart Grid.

[10]  Bin Wang,et al.  Formulation and Characterization of Power System Electromechanical Oscillations , 2016 .

[11]  Tao Zhang,et al.  Synchrophasor-Based Dominant Electromechanical Oscillation Modes Extraction Using OpDMD Considering Measurement Noise , 2019, IEEE Systems Journal.

[12]  S. R. Ibrahim,et al.  Random Decrement: Identification of Structures Subjected to Ambient Excitation , 1998 .

[13]  Nina F. Thornhill,et al.  Comparative review of methods for stability monitoring in electrical power systems and vibrating structures , 2010 .

[14]  Nina F. Thornhill,et al.  A Dynamic Mode Decomposition Framework for Global Power System Oscillation Analysis , 2015, IEEE Transactions on Power Systems.

[15]  Ning Zhou,et al.  Initial results in power system identification from injected probing signals using a subspace method , 2006, IEEE Transactions on Power Systems.

[16]  Vaithianathan Mani Venkatasubramanian,et al.  Modal Analysis of Ambient PMU Measurements Using Orthogonal Wavelet Bases , 2015, IEEE Transactions on Smart Grid.

[17]  Enio Vasconcelos Filho,et al.  A Dynamic Mode Decomposition Approach With Hankel Blocks to Forecast Multi-Channel Temporal Series , 2019, IEEE Control Systems Letters.

[18]  Y. Min,et al.  Oscillation Energy Analysis of Inter-Area Low-Frequency Oscillations in Power Systems , 2016, IEEE Transactions on Power Systems.

[19]  Bingni W. Brunton,et al.  Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition , 2014, Journal of Neuroscience Methods.

[20]  Vaithianathan Venkatasubramanian,et al.  Electromechanical mode estimation using recursive adaptive stochastic subspace identification , 2014, 2014 IEEE PES T&D Conference and Exposition.

[21]  Graham Rogers,et al.  Power System Oscillations , 1999 .

[22]  Vaithianathan Venkatasubramanian,et al.  Recursive Frequency Domain Decomposition for Multidimensional Ambient Modal Estimation , 2017, IEEE Transactions on Power Systems.

[23]  Xueping Pan,et al.  Oscillation modal analysis from ambient synchrophasor data using distributed frequency domain optimization , 2013, IEEE Transactions on Power Systems.

[24]  P. Schmid,et al.  Dynamic mode decomposition of numerical and experimental data , 2008, Journal of Fluid Mechanics.

[25]  Steven L. Brunton,et al.  On dynamic mode decomposition: Theory and applications , 2013, 1312.0041.

[26]  Hao Zhu,et al.  Data-Driven Estimation of Frequency Response From Ambient Synchrophasor Measurements , 2018, IEEE Transactions on Power Systems.

[27]  Jukka Turunen,et al.  Modal analysis of power systems through natural excitation technique , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.