Wide Area Inter-Area Oscillation Monitoring Using Fast Nonlinear Estimation Algorithm

This paper presents the results of the development of Smart Grid transmission network applications in the Great Britain (GB) power system. A new Wide Area Monitoring System (WAMS) application for monitoring inter-area oscillations is developed. The core of this novel application is a fast nonlinear algorithm for the real-time estimation of the dominant inter-area oscillation mode, which processes GPS synchronized information obtained from Phasor Measurement Units (PMUs) installed in the power system. It is based on the Newton-Type Algorithm (NTA), an efficient parameter estimator. The paper focuses on the practical application of the new WAMS application: two data sets were tested, one based on computer simulations and the other based on real-life data records. The computer simulated oscillatory signals were obtained through dynamic simulations of the full GB power system model consisting of over 200 generators. The real-life data records used information collected by the FlexNet Wide Area Monitoring System (FlexNET-WAMS) installed in the GB network. Based on these data records, the features of inter-area oscillations in the GB network are drawn.

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

[2]  Petr Korba.  Real-time monitoring of electromechanical oscillations in power systems: first findings , 2007 .

[3]  J. F. Hauer,et al.  Application of Prony analysis to the determination of modal content and equivalent models for measured power system response , 1991 .

[4]  V. Terzija,et al.  Two-step spectral clustering controlled islanding algorithm , 2013, 2013 IEEE Power & Energy Society General Meeting.

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

[6]  J.W. Pierre,et al.  Use of least-mean squares (LMS) adaptive filtering technique for estimating low-frequency electromechanical modes in power systems , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[7]  A. M. Carter,et al.  Comparison of Three Electromechanical Oscillation Damping Estimation Methods , 2011, IEEE Transactions on Power Systems.

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

[9]  Vladimir Terzija,et al.  Voltage phasor and local system frequency estimation using Newton type algorithm , 1994 .

[10]  Tianshu Bi,et al.  Measurements get together , 2009, IEEE Power and Energy Magazine.

[11]  P. O'Shea,et al.  A High Resolution Spectral Analysis Algorithm for Power System Disturbance Monitoring , 2002, IEEE Power Engineering Review.

[12]  A. R. Messina,et al.  A refined Hilbert-Huang transform with applications to inter-area oscillation monitoring , 2009 .

[13]  Milenko B. Djurić,et al.  A new self-tuning algorithm for the frequency estimation of distorted signals , 1995 .

[14]  Ning Zhou,et al.  Performance of three mode-meter block-processing algorithms for automated dynamic stability assessment , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[15]  P. Regulski,et al.  FlexNet wide area monitoring system , 2011, 2011 IEEE Power and Energy Society General Meeting.

[16]  J. F. Hauer,et al.  Initial results in Prony analysis of power system response signals , 1990 .

[17]  L. Scharf,et al.  A Prony method for noisy data: Choosing the signal components and selecting the order in exponential signal models , 1984, Proceedings of the IEEE.

[18]  Nirmal-Kumar C. Nair,et al.  Adaptive sampling scheme for monitoring oscillations using Prony analysis , 2009 .

[19]  Kjetil Uhlen,et al.  Wide-area monitoring of electromechanical oscillations in the nordic power system: practical experience , 2010 .

[20]  N. Kakimoto,et al.  Monitoring of interarea oscillation mode by synchronized phasor measurement , 2006, IEEE Transactions on Power Systems.

[21]  I. Erlich,et al.  Wavelet-Based Analysis of Power System Low-Frequency Electromechanical Oscillations , 2011, IEEE Transactions on Power Systems.

[22]  Ning Zhou,et al.  Robust RLS Methods for Online Estimation of Power System Electromechanical Modes , 2007, IEEE Transactions on Power Systems.

[23]  J. W. Pierre,et al.  Use of ARMA Block Processing for Estimating Stationary Low-Frequency Electromechanical Modes of Power Systems , 2002, IEEE Power Engineering Review.

[24]  A.R. Messina,et al.  A Refined Hilbert–Huang Transform With Applications to Interarea Oscillation Monitoring , 2009, IEEE Transactions on Power Systems.

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

[26]  Vahid Madani,et al.  Wide-Area Monitoring, Protection, and Control of Future Electric Power Networks , 2011, Proceedings of the IEEE.

[27]  W. Mittelstadt,et al.  Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods , 2008, IEEE Transactions on Power Systems.

[28]  C. W. Taylor.  Improving grid behaviour , 1999 .

[29]  D.J. Trudnowski,et al.  Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[30]  Xiaorong Xie,et al.  Dynamic tracking of low-frequency oscillations with improved Prony method in wide-area measurement system , 2004, IEEE Power Engineering Society General Meeting, 2004..