Empirical estimation approach to the study of inter-area resonance interactions in power systems

Abstract Studies to deal with the identification of inter-area resonance interactions by fault diagnosis and historical databases at interconnected power systems have led to the development of new methods to delimit and split resonant regions and their control area regulations. This paper contributes in employing the frequency-domain empirical orthogonal functions (EOFs) for the measurement-based analysis of the inter-area resonance modes and its neighborhood. This multi-resolution empirical approach, developed for multiple-input multiple-output (MIMO) systems, is considered to contribute in understanding the spatial orientation properties and the dynamical/physical behavior of inter-area resonance interactions. Also, the method contributes much in advancing our knowledge of inter-area phase-matching patterns (PMPs) modes and their phase shifts from large data sets. In order to demonstrate the usefulness of obtaining and delimiting neighboring areas with resonant interactions in interconnected power systems, the proposed method is numerically applied in detail to the IEEE 16-generator 68-bus test system.

[1]  Peter W. Sauer,et al.  Is strong modal resonance a precursor to power system oscillations , 2001 .

[2]  Boming Zhang,et al.  A Fast Method to Identify the Order of Frequency-Dependent Network Equivalents , 2016, IEEE Transactions on Power Systems.

[3]  An Luo,et al.  Harmonic resonance characteristics of large-scale distributed power plant in wideband frequency domain , 2017 .

[4]  C. E. Castañeda,et al.  Reduced-order equivalent model to large power networks derived from its spectral dispersion , 2017 .

[5]  D. Wilcox Numerical Laplace Transformation and Inversion , 1978 .

[6]  A. Semlyen,et al.  Rational approximation of frequency domain responses by vector fitting , 1999 .

[7]  N.-K.C. Nair,et al.  Effects of sampling in monitoring power system oscillations using on-line Prony analysis , 2008, 2008 Australasian Universities Power Engineering Conference.

[8]  John M. Wallace,et al.  Empirical Orthogonal Representation of time series in the frequency domain , 1972 .

[9]  Thomas Kailath,et al.  Linear Systems , 1980 .

[10]  A. R. Messina,et al.  Nonlinear, non-stationary analysis of interarea oscillations via Hilbert spectral analysis , 2006, IEEE Transactions on Power Systems.

[11]  Yilu Liu,et al.  Identification of Interarea Modes From Ringdown Data by Curve-Fitting in the Frequency Domain , 2017, IEEE Transactions on Power Systems.

[12]  Tarek A. Mahmoud,et al.  Observer-based echo-state neural network control for a class of nonlinear systems , 2018, Trans. Inst. Meas. Control.

[13]  J. Wallace,et al.  Empirical Orthogonal Representation of Time Series in the Frequency Domain. Part I: Theoretical Considerations , 1972 .

[15]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[16]  Vaithianathan Venkatasubramanian,et al.  Inter-Area Resonance in Power Systems From Forced Oscillations , 2016, IEEE Transactions on Power Systems.

[17]  Balu Santhanam,et al.  Discrete Gauss-Hermite Functions and Eigenvectors of the Centered Discrete Fourier Transform , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[18]  Cheng Lu,et al.  Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  A. Ramirez,et al.  Vector Fitting-Based Calculation of Frequency-Dependent Network Equivalents by Frequency Partitioning and Model-Order Reduction , 2009, IEEE Transactions on Power Delivery.

[21]  Taehyoun Kim,et al.  Frequency-Domain Karhunen -Loeve Method and Its Application to Linear Dynamic Systems , 1998 .

[22]  Abner Ramirez,et al.  A novel frequency-domain approach for distributed harmonic analysis of multi-area interconnected power systems , 2017 .

[23]  E. Barocio,et al.  Perturbations of weakly resonant power system electromechanical modes , 2005, IEEE Transactions on Power Systems.

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

[25]  Fujun Ma,et al.  Large-scale photovoltaic plant harmonic transmission model and analysis on resonance characteristics , 2015 .

[26]  Hassan Sayyaadi,et al.  Intelligent control of an MR prosthesis knee using of a hybrid self-organizing fuzzy controller and multidimensional wavelet NN , 2017 .

[27]  J. Hammond,et al.  A non-parametric approach for linear system identification using principal component analysis , 2007 .

[28]  Jun Liang,et al.  Study of resonance in wind parks , 2015 .

[29]  Sukumar Kamalasadan,et al.  Application of Balanced Realizations for Model-Order Reduction of Dynamic Power System Equivalents , 2016, IEEE Transactions on Power Delivery.

[30]  Hoang Le-Huy,et al.  Modeling and simulation of the propagation of harmonics in electric power networks. I: Concepts, models, and simulation techniques. Discussion , 1996 .

[31]  A. R. Messina,et al.  A real normal form approach to the study of resonant power systems , 2006, IEEE Transactions on Power Systems.

[32]  Wilsun Xu,et al.  Harmonic resonance mode analysis , 2005 .

[33]  Chandan Kumar,et al.  Modal resonance in power system-A case study , 2014, 2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).

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