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Abstract
This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques.
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References
- Abhisek U (2007), Intelligent systems and signal processing in power engineering. pringer- Verlag, Switzerland.
- Arora CM, Surana SL (1996), Transient security evaluation and preventive control of power systems using PR techniques. IE (India) 76:199-203.
- Azah MS, Maniruzzaman HA (2001), Static security assessment of a power system using geneticbased neural networks. Electric Power Components and Systems 29:1111-1121.
- Boudour M, Hellal A (2006), Combined use of unsupervised
- and supervised learning for large scale power system static security assessment. Int. J. of Power & Energy Systems 26(2):157-163.
- Chih-Chung C, Chih-Jen L (2001), LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Chih-Wei H, Chih-Jen L (2005), A comparison of methods for multi-class support vector machines. Taiwan (cjlin@csie.ntu.edu.tw).
- Haghifam MR, Zebarjadi V (1996), Fuzzy logic and neural network approach to static security assessment for electric power systems. Proceedings of 4th European Congress on Intelligent Techniques and Soft Computing 3:2009-2013. http://www.ee.washington.edu/research/pstca (Power System Test Case Archive). http://www.pserc.cornell.edu/matpower (Matpower 3.2).
- Huang SJ (2001), Static security assessment of a power system using query-based learning approaches with genetic enhancement. IEE Proceedings-Generation, Transmission & Distribution 148(4):319-325.
- Kai-Bo D, Sathiya KS (2005), Which is the best multi-class SVM method? An empirical study. Springer-verlag, Berlin Heidelberg 278-285. Laveen K (1974), Patterns in pattern recognition. IEEE Transactions on Information Theory IT- 20(6):697-722.
- Lo KL, Peng LJ (1997), Design of artificial neural networks for on-line static security assessment problems. Proc. of the 4th Int. Conf. on APSCOM-97, Hong Kong 288-293.
- Luan WP, Lo KL, Yu YX (2000), ANN based pattern recognition technique for power system security assessment. IEEE Int. Conf. on Electric Utility Deregulation, Restructuring and Power Technologies, London 197-202.
- Min JHY, Chan L (2005), Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 28(5):603-614.
- Pang CK, Kovio AJ, El-Abiad, AH (1973), Application of pattern recognition to steady state security evaluation in a power system. IEEE Transactions on Systems, Mans and Cybernetics SMC-3(6):622-631.
- Pang CK, Prabhakara FS, El-Abiad AH, Koivo AJ (1974), Security evaluation in power systems using pattern recognition. IEEE Transactions on Power Apparatus & Systems PAS-93:969-976.
- Pecas LJA, Machiel BFP, Marques DSJP (1988), On-line transient stability assessment and enhancement by pattern recognition techniques. Electrical Machines and Power Systems 25:293- 310.
- Sa DCJMG, Munro N (1984), Pattern recognition in power system security. Int. J. of Electrical Power & Energy Systems 6(1):31-36.
- Saeh IS, Khairuddin A (2008), Static security assessment using artificial neural network. IEEE 2nd Int. Power & Energy Conference (PECon'08) 1172- 1178.
- Shahidehpour SM (2003), Communication and control in electric power systems. Wiley Interscience, John Wiley & Sons, Third Edition.
- Siri W, Sharkawi MAEl (1992), Feature selection for static security assessment using neural networks. IEEE Int. Symposium on Circuits & Systems, San Diego, California 10-13:1693-1696.
- Swarup KS, Corthis BP (2006), Power system static security assessment using self-organizing neural network. J. of Indian Institute of Science 86(4):327-342.
References
Abhisek U (2007), Intelligent systems and signal processing in power engineering. pringer- Verlag, Switzerland.
Arora CM, Surana SL (1996), Transient security evaluation and preventive control of power systems using PR techniques. IE (India) 76:199-203.
Azah MS, Maniruzzaman HA (2001), Static security assessment of a power system using geneticbased neural networks. Electric Power Components and Systems 29:1111-1121.
Boudour M, Hellal A (2006), Combined use of unsupervised
and supervised learning for large scale power system static security assessment. Int. J. of Power & Energy Systems 26(2):157-163.
Chih-Chung C, Chih-Jen L (2001), LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Chih-Wei H, Chih-Jen L (2005), A comparison of methods for multi-class support vector machines. Taiwan (cjlin@csie.ntu.edu.tw).
Haghifam MR, Zebarjadi V (1996), Fuzzy logic and neural network approach to static security assessment for electric power systems. Proceedings of 4th European Congress on Intelligent Techniques and Soft Computing 3:2009-2013. http://www.ee.washington.edu/research/pstca (Power System Test Case Archive). http://www.pserc.cornell.edu/matpower (Matpower 3.2).
Huang SJ (2001), Static security assessment of a power system using query-based learning approaches with genetic enhancement. IEE Proceedings-Generation, Transmission & Distribution 148(4):319-325.
Kai-Bo D, Sathiya KS (2005), Which is the best multi-class SVM method? An empirical study. Springer-verlag, Berlin Heidelberg 278-285. Laveen K (1974), Patterns in pattern recognition. IEEE Transactions on Information Theory IT- 20(6):697-722.
Lo KL, Peng LJ (1997), Design of artificial neural networks for on-line static security assessment problems. Proc. of the 4th Int. Conf. on APSCOM-97, Hong Kong 288-293.
Luan WP, Lo KL, Yu YX (2000), ANN based pattern recognition technique for power system security assessment. IEEE Int. Conf. on Electric Utility Deregulation, Restructuring and Power Technologies, London 197-202.
Min JHY, Chan L (2005), Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 28(5):603-614.
Pang CK, Kovio AJ, El-Abiad, AH (1973), Application of pattern recognition to steady state security evaluation in a power system. IEEE Transactions on Systems, Mans and Cybernetics SMC-3(6):622-631.
Pang CK, Prabhakara FS, El-Abiad AH, Koivo AJ (1974), Security evaluation in power systems using pattern recognition. IEEE Transactions on Power Apparatus & Systems PAS-93:969-976.
Pecas LJA, Machiel BFP, Marques DSJP (1988), On-line transient stability assessment and enhancement by pattern recognition techniques. Electrical Machines and Power Systems 25:293- 310.
Sa DCJMG, Munro N (1984), Pattern recognition in power system security. Int. J. of Electrical Power & Energy Systems 6(1):31-36.
Saeh IS, Khairuddin A (2008), Static security assessment using artificial neural network. IEEE 2nd Int. Power & Energy Conference (PECon'08) 1172- 1178.
Shahidehpour SM (2003), Communication and control in electric power systems. Wiley Interscience, John Wiley & Sons, Third Edition.
Siri W, Sharkawi MAEl (1992), Feature selection for static security assessment using neural networks. IEEE Int. Symposium on Circuits & Systems, San Diego, California 10-13:1693-1696.
Swarup KS, Corthis BP (2006), Power system static security assessment using self-organizing neural network. J. of Indian Institute of Science 86(4):327-342.