Power System Security Assessment using Binary SVM Based Pattern Recognition
Power System Security is a major concern in real time
operation. Conventional method of security evaluation consists of
performing continuous load flow and transient stability studies by
simulation program. This is highly time consuming and infeasible
for on-line application. Pattern Recognition (PR) is a promising
tool for on-line security evaluation. This paper proposes a Support
Vector Machine (SVM) based binary classification for static and
transient security evaluation. The proposed SVM based PR approach
is implemented on New England 39 Bus and IEEE 57 Bus systems.
The simulation results of SVM classifier is compared with the other
classifier algorithms like Method of Least Squares (MLS), Multi-
Layer Perceptron (MLP) and Linear Discriminant Analysis (LDA)
classifiers.
[1] K.R.Niazi, C.M.Arora, S.L.Surana, "Power system security evaluation
using ANN: Feature Selection using Divergence", Electric Power Systems
Research, Vol. 69, Issues 2-3, May 2004, pp.161-167.
[2] Daniel S. Kirschen, "Power System Security", Power Engineering
Journal, October 2002, pp. 241-248.
[3] Prabha Kundur, John Paserba, Venkat Ajjarapu, Goran Anderson, Anjan
Bose, Claudio Canizares, Nikos Hatziargyriou, David Hill, Alex
Stankovic, Carson Taylor, Th.Van Cutsem, Vijay Vittal, "Definition and
Classification of Power System Stability", IEEE Transactions on Power
Systems, Vol. 19, No. 2, May 2004, pp. 1387-1401.
[4] Craig A.Jensen, Mohamed A.El.Sharkawi, Robert J.Marks, "Power
System Security Assessment using Neural Networks: Feature Selection
using Fisher Discrimination", IEEE Transactions on Power Systems, Vol.
16, No. 4, Nov 2001, pp.757-763.
[5] Sa Da Costa, N.Munro, "Pattern Recognition in Power System Security",
International Journal of Electrical Power & Energy Systems, Vol. 6, No.
1, Jan 1984, pp. 31-36.
[6] C.K. Pang, F.S. Prabhakara, Ahmed H. El-Abiad, A.J. Koivo, "Security
Evaluation in Power Systems using Pattern Recognition", IEEE Transactions
on Power Apparatus and Systems, Vol. PAS-93, May/June 1974,
pp. 969-976.
[7] I.S.Saeh, A.Khairuddin, "Decision Tree for Static Security Assessment
and Classification", International Conference on Future Computer and
Communication (ICFCC), 2009, pp.681-684.
[8] I.S.Saeh, A.Khairuddin, "Static Security Assessment using Artificial
Neural Network", IEEE 2nd International Conference on Power &
Energy (PECon 2008), 2008, pp.1172-1178.
[9] Leonard L.Grigsby, "Power System Stability and Control", Electric
Power Engineering Handbook, Second Edition, CRC Press, 2007.
[10] Chok K.Pang, Antti J.Kovio, Ahmed H.El.Abiad, "Application of Pattern
Recognition to Steady State Security Evaluation in a Power System",
IEEE Transactions on Systems, Mans & Cybernetics, Vol. SMC-3, No.
6, Nov 1973, pp. 622-631.
[11] M. Shahidehpour, "Communication and Control in Electric Power Systems",
Wiley Interscience, John Wiley & Sons, 2003, pp. 265-270.
[12] J.C.S.Souza, M.B.Do Coutto, M.Th.Schilling, "Fast Contingency Selection
through a Pattern Analysis approach", Electric Power Systems
Research, Vol. 62, Issue 1, May 2002, pp. 13-19.
[13] Hossein Hakim, "Application of Pattern Recognition in Transient Security
Assessment", Electric Power Components and Systems, Vol. 20,
Issue 1, January 1992, pp. 1-15.
[14] D. Duttam, "Trends in Pattern Recognition and Machine Learning",
Defence Science Journal, Vol. 35, No. 3, July 1985, pp. 327-351.
[15] Se-Young Oh, "Pattern Recognition and Associative Memory Approach
to Power System Security Assessment", IEEE Transactions on Systems,
Man and Cybernetics, Vol. SMC-16, No. 1, Jan/Feb 1986, pp. 62-72.
[16] Abhisek Ukil, "Intelligent Systems and Signal Processing in Power
Engineering", -Springer-Verlag, 2007.
[17] L.S.Moulin, A.P.Alves Da Silva, M.A.El-Sharkawi, Robert J. Marks,
"Support Vector Machines for Transient Stability Analysis of Large-
Scale Power Systems", IEEE Transactions on Power Systems, Vol. 19,
No. 2, May 2004, pp. 818-825.
[18] Jae H.Min, Young-Chan Lee, "Bankruptcy Prediction using Support
Vector Machine with Optimal Choice of Kernel Function Parameters",
Expert Systems with Application, Vol. 28, Issue 5, 2005, pp. 603-614.
[19] M.A.Pai, "Computer Techniques in Power System Analysis", Tata
McGraw Hill, Second Edition, 1979.
[20] http:// www.pserc.cornell.edu/matpower
[21] Chin-Chung Chang, Chih-Jen Lin, "LIBSVM: A Library for Support
Vector Machines", Software available at www.csie.ntu.edu.tw / cjlin/.
[1] K.R.Niazi, C.M.Arora, S.L.Surana, "Power system security evaluation
using ANN: Feature Selection using Divergence", Electric Power Systems
Research, Vol. 69, Issues 2-3, May 2004, pp.161-167.
[2] Daniel S. Kirschen, "Power System Security", Power Engineering
Journal, October 2002, pp. 241-248.
[3] Prabha Kundur, John Paserba, Venkat Ajjarapu, Goran Anderson, Anjan
Bose, Claudio Canizares, Nikos Hatziargyriou, David Hill, Alex
Stankovic, Carson Taylor, Th.Van Cutsem, Vijay Vittal, "Definition and
Classification of Power System Stability", IEEE Transactions on Power
Systems, Vol. 19, No. 2, May 2004, pp. 1387-1401.
[4] Craig A.Jensen, Mohamed A.El.Sharkawi, Robert J.Marks, "Power
System Security Assessment using Neural Networks: Feature Selection
using Fisher Discrimination", IEEE Transactions on Power Systems, Vol.
16, No. 4, Nov 2001, pp.757-763.
[5] Sa Da Costa, N.Munro, "Pattern Recognition in Power System Security",
International Journal of Electrical Power & Energy Systems, Vol. 6, No.
1, Jan 1984, pp. 31-36.
[6] C.K. Pang, F.S. Prabhakara, Ahmed H. El-Abiad, A.J. Koivo, "Security
Evaluation in Power Systems using Pattern Recognition", IEEE Transactions
on Power Apparatus and Systems, Vol. PAS-93, May/June 1974,
pp. 969-976.
[7] I.S.Saeh, A.Khairuddin, "Decision Tree for Static Security Assessment
and Classification", International Conference on Future Computer and
Communication (ICFCC), 2009, pp.681-684.
[8] I.S.Saeh, A.Khairuddin, "Static Security Assessment using Artificial
Neural Network", IEEE 2nd International Conference on Power &
Energy (PECon 2008), 2008, pp.1172-1178.
[9] Leonard L.Grigsby, "Power System Stability and Control", Electric
Power Engineering Handbook, Second Edition, CRC Press, 2007.
[10] Chok K.Pang, Antti J.Kovio, Ahmed H.El.Abiad, "Application of Pattern
Recognition to Steady State Security Evaluation in a Power System",
IEEE Transactions on Systems, Mans & Cybernetics, Vol. SMC-3, No.
6, Nov 1973, pp. 622-631.
[11] M. Shahidehpour, "Communication and Control in Electric Power Systems",
Wiley Interscience, John Wiley & Sons, 2003, pp. 265-270.
[12] J.C.S.Souza, M.B.Do Coutto, M.Th.Schilling, "Fast Contingency Selection
through a Pattern Analysis approach", Electric Power Systems
Research, Vol. 62, Issue 1, May 2002, pp. 13-19.
[13] Hossein Hakim, "Application of Pattern Recognition in Transient Security
Assessment", Electric Power Components and Systems, Vol. 20,
Issue 1, January 1992, pp. 1-15.
[14] D. Duttam, "Trends in Pattern Recognition and Machine Learning",
Defence Science Journal, Vol. 35, No. 3, July 1985, pp. 327-351.
[15] Se-Young Oh, "Pattern Recognition and Associative Memory Approach
to Power System Security Assessment", IEEE Transactions on Systems,
Man and Cybernetics, Vol. SMC-16, No. 1, Jan/Feb 1986, pp. 62-72.
[16] Abhisek Ukil, "Intelligent Systems and Signal Processing in Power
Engineering", -Springer-Verlag, 2007.
[17] L.S.Moulin, A.P.Alves Da Silva, M.A.El-Sharkawi, Robert J. Marks,
"Support Vector Machines for Transient Stability Analysis of Large-
Scale Power Systems", IEEE Transactions on Power Systems, Vol. 19,
No. 2, May 2004, pp. 818-825.
[18] Jae H.Min, Young-Chan Lee, "Bankruptcy Prediction using Support
Vector Machine with Optimal Choice of Kernel Function Parameters",
Expert Systems with Application, Vol. 28, Issue 5, 2005, pp. 603-614.
[19] M.A.Pai, "Computer Techniques in Power System Analysis", Tata
McGraw Hill, Second Edition, 1979.
[20] http:// www.pserc.cornell.edu/matpower
[21] Chin-Chung Chang, Chih-Jen Lin, "LIBSVM: A Library for Support
Vector Machines", Software available at www.csie.ntu.edu.tw / cjlin/.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59880", author = "S Kalyani and K Shanti Swarup", title = "Power System Security Assessment using Binary SVM Based Pattern Recognition", abstract = "Power System Security is a major concern in real time
operation. Conventional method of security evaluation consists of
performing continuous load flow and transient stability studies by
simulation program. This is highly time consuming and infeasible
for on-line application. Pattern Recognition (PR) is a promising
tool for on-line security evaluation. This paper proposes a Support
Vector Machine (SVM) based binary classification for static and
transient security evaluation. The proposed SVM based PR approach
is implemented on New England 39 Bus and IEEE 57 Bus systems.
The simulation results of SVM classifier is compared with the other
classifier algorithms like Method of Least Squares (MLS), Multi-
Layer Perceptron (MLP) and Linear Discriminant Analysis (LDA)
classifiers.", keywords = "Static Security, Transient Security, Pattern Recognition,Classifier, Support Vector Machine.", volume = "3", number = "4", pages = "926-7", }
