Measurement Based Method for Online Characterization of Generator Dynamic Behaviour in Systems With Renewable Generation

This paper introduces a two-step methodology for online identification of the participation of generators in power system oscillatory modes, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with the operation of modern power systems with renewable energy sources (RESs) are investigated as well as the impact of the dynamic behavior of power electronic interfaced RESs.

[1]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[2]  James S. Thorp,et al.  Decision trees for real-time transient stability prediction , 1994 .

[3]  Jovica V. Milanović,et al.  Probabilistic Framework for Online Identification of Dynamic Behavior of Power Systems With Renewable Generation , 2018, IEEE Transactions on Power Systems.

[4]  D. Trudnowski,et al.  A stepwise regression method for estimating dominant electromechanical modes , 2012, 2012 IEEE Power and Energy Society General Meeting.

[5]  M. A. M. Ariff,et al.  Coherency identification in interconnected power system - an independent component analysis approach , 2013, 2013 IEEE Power & Energy Society General Meeting.

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

[7]  Udaya Annakkage,et al.  Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements , 2011, 2011 IEEE Power and Energy Society General Meeting.

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

[9]  Jovica V. Milanović,et al.  Probabilistic Framework for Transient Stability Assessment of Power Systems With High Penetration of Renewable Generation , 2017, IEEE Transactions on Power Systems.

[10]  M. Jonsson,et al.  A new method suitable for real-time generator coherency determination , 2004, IEEE Transactions on Power Systems.

[11]  Miao He,et al.  Robust Online Dynamic Security Assessment Using Adaptive Ensemble Decision-Tree Learning , 2013, IEEE Transactions on Power Systems.

[12]  J.J. Sanchez-Gasca Computation of turbine-generator subsynchronous torsional modes from measured data using the eigensystem realization algorithm , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[13]  Panagiotis N. Papadopoulos,et al.  Feasibility study of applicability of recurrence quantification analysis for clustering of power system dynamic responses , 2016, 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[14]  N. Amjady,et al.  Transient Stability Prediction by a Hybrid Intelligent System , 2007, IEEE Transactions on Power Systems.

[15]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[16]  Vaithianathan Venkatasubramanian,et al.  Multi-Dimensional Fourier Ringdown Analysis for Power Systems Using Synchrophasors , 2014, IEEE Transactions on Power Systems.

[17]  Eleftherios O. Kontis,et al.  Measurement-Based Hybrid Approach for Ringdown Analysis of Power Systems , 2016 .

[18]  M. Crow,et al.  The matrix pencil for power system modal extraction , 2005, IEEE Transactions on Power Systems.

[19]  Janusz Bialek,et al.  Classification of mode damping and amplitude in power systems using synchrophasor measurements and classification trees , 2013, IEEE Transactions on Power Systems.

[20]  N. Senroy,et al.  Decision Tree Assisted Controlled Islanding , 2006, IEEE Transactions on Power Systems.