Dynamic small disturbance voltage instability prediction based on wide area measurement system

In this paper an online method to predict the small disturbance voltage instability based on wide area measurement system is proposed. The proposed method splits the voltage synchrophasors into high frequency and low frequency signals to extract proper features to accurately predict the voltage instability using SVM-based classifier. The proposed method is implemented and tested on slightly modified version of Nordic32 test system. To examine the efficiency of the proposed method, the stator current limiters as well as the on-load tap changing transformers are considered in test system. Simulation results have shown that the proposed method effectively predicts the voltage instability occurrence two seconds just after the disturbance in this case study.

[1]  Lijun Zhang,et al.  Multiscale morphology analysis and its application to fault diagnosis , 2008 .

[2]  M. Begovic,et al.  Use of local measurements to estimate voltage-stability margin , 1997, Proceedings of the 20th International Conference on Power Industry Computer Applications.

[3]  Desmond J. Higham,et al.  Long-Term Dynamics , 2010 .

[4]  A.C.Z. de Souza,et al.  New techniques to speed up voltage collapse computations using tangent vectors , 1997 .

[5]  S. M. Shahrtash,et al.  On-line small disturbance voltage stability assessment in power systems based on wide area measurement , 2011, 2011 10th International Conference on Environment and Electrical Engineering.

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

[7]  T. Y. Ji,et al.  Protective Relaying of Power Systems Using Mathematical Morphology , 2009 .

[8]  M. Glavic,et al.  Wide-Area Detection of Voltage Instability From Synchronized Phasor Measurements. Part I: Principle , 2009, IEEE Transactions on Power Systems.

[9]  Athula D. Rajapakse,et al.  Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network , 2010 .

[10]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[11]  L. D. Arya,et al.  Technique for voltage stability assessment using newly developed line voltage stability index , 2008 .

[12]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[13]  M. Parniani,et al.  A Fast Local Index for Online Estimation of Closeness to Loadability Limit , 2010, IEEE Transactions on Power Systems.

[14]  Robert A. Schlueter.  A voltage stability security assessment method , 1998 .

[15]  N. S. Marimuthu,et al.  Support Vector Machine for Discrimination Between Fault and Magnetizing Inrush Current in Power Transformer , 2007 .

[16]  K. R. Padiyar,et al.  Power system dynamics : stability and control , 1996 .

[17]  M. Glavic,et al.  Detecting with PMUs the onset of voltage instability caused by a large disturbance , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.