Artificial neural network for power system dynamic stability recognition
Corressponding author's email:
aunn@hcmute.edu.vnKeywords:
dynamic stability recognition, power system, neural network, featuresAbstract
Feature selection is very important data-processing technique in dynamic stability rec- ognition of power systems. This paper presents a ranking method for feature selection that bases on Fisher, Divergence, Bhattacharyya distance and Correlation coefficient. The aim is to select features with the highest data discrimination. Multilayer Feed-forward Neural Network has been applied for power system dynamic stability recognition. The proposed approach has been tested on the IEEE 30-bus power system. Many experiments have been done with the aim of finding parameters for optimal neural network performance. In comparison between Levenberg- Marquardt and Scaled Conjugate Gradient learning algorithms, results show that Levenberg-Marquardt algorithm with Fisher distance for feature selection yields less number of features and higher recognition accuracy than the others.
Downloads: 0
References
A. Y. Abdelaziz and M. A. El-Dessouki, “Transient Stability Assessment using Decision Trees and Fuzzy Logic Techniques,” Int. J. Intell. Syst. Appl., vol. 5, no. 10, pp. 1–10, 2013.
R. Zhang, H. K. Polytechnic, H. Kong, Y. Xu, Z. Y. Dong, K. Meng, and Z. Xu, “Intel- ligent Systems for Power System Dynamic Security Assessment: Review and Classifica- tion,” pp. 134–139, 2011.
A. M. a. Haidar, M. W. Mustafa, F. a. F. Ibrahim, and I. a. Ahmed, “Transient Stabil- ity Evaluation Of Electrical Power System Using Generalized Regression Neural Net- works,” Appl. Soft Comput., vol. 11, no. 4, pp. 3558–3570, 2011.
K. S. Swarup, “Artificial Neural Network Using Pattern Recognition For Security As- sessment And Analysis,” Neurocomputing, vol. 71, no. 4–6, pp. 983–998, 2008.
R. Zhang, S. Member, Y. Xu, and Z. Y. Dong, “Feature Selection For Intelligent Stabil- ity Assessment of Power Systems,” 2012 IEEE Power Energy Soc. Gen. Meet., pp. 1–7, 2012.
S. Haykin, ‘’Neural Networks and Learning Machines’’, Third Edition, Pearson Prentice Hall, 2009.
] K. Y. Lee and M. A. El-Sharkawi,’’Modern Heuristic Optimization Techniques’’, A John Wiley & Sons. Inc. Publication, 2008.
A. R. Webb and K. D. Copsey, “Statistical Pattern Recognition”, Third Edition, A John Wiley & Sons. Inc. Publication, 2011.
] M. Cheriet, N. Kharma, C.-L. Liu, and C. Y. Suen, “Character Recognition Systems - A Guide for Students and Practioners”, A John Wiley & Sons. Inc. Publication, 2007.
Quyen Huy Anh, “The applycation of pattern recognition for fast analysis of the dynamic stability of electrical power system,” Perganon, pp. 1–13, 1994.
J. D. Glover, M. S. Sarma, and T. Overbye, “Power System Analysis and Design”, Fifth Edition, Global Engineering: Christopher M.Shortt, 2012.
M.H.Beale, M.T.Hagan, H.B.Demuth,“Neural Network Toolbox User’s Guide ”, 2015.
Downloads
Published
How to Cite
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © JTE.
