Multi-Goal Feature Selection Function in Binary Particle Swarm Optimization for Power System Stability Classification
Received: 20 February 2023 | Revised: 3 March 2023 | Accepted: 8 March 2023 | Online: 2 April 2023
Corresponding author: Trong Nghia Le
Abstract
The input feature is vital for power system stability classification. Feature selection can reduce the size of the input feature, making classifier training easier, and the small size of the input feature subset also reduces the cost of purchasing sensor measurement equipment. Therefore, feature selection works require an efficient seeking method. Binary Particle Swarm Optimization (BPSO) is a simple and easy-to-implement evolutionary calculation technique. While the classification accuracy of BPSO is essential, it is also needed to drive the algorithm to minimize the number of variables. The proposed approach is to apply a multi-objective function to help the BPSO algorithm identify the subset of features that can achieve the minimum number of variables and the highest classification accuracy. The k-nearest neighbor classifier is employed in the experiments to evaluate the classification performance on the IEEE 39-bus dataset.
Keywords:
feature selection, classification, power system stability, BPSO, K-nearest neighbor classifierDownloads
References
K. Teeparthi and D. M. V. Kumar, "Dynamic Power System Security Analysis Using a Hybrid PSO-APO Algorithm," Engineering, Technology & Applied Science Research, vol. 7, no. 6, pp. 2124–2131, Dec. 2017. DOI: https://doi.org/10.48084/etasr.1477
Y. V. Makarov, V. I. Reshetov, A. Stroev, and I. Voropai, "Blackout Prevention in the United States, Europe, and Russia," Proceedings of the IEEE, vol. 93, no. 11, pp. 1942–1955, Aug. 2005. DOI: https://doi.org/10.1109/JPROC.2005.857486
H. F. Khan, A. H. Hanif, and N. Anwar, "Rotor Angle and Voltage Stability Analysis with Fault Location Identification on the IEEE 9 Bus System," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5259–5264, Feb. 2020. DOI: https://doi.org/10.48084/etasr.3175
D. V. Ngo, K. V. Pham, D. D. Le, K. H. Le, and K. V. Huynh, "Assessing Power System Stability Following Load Changes and Considering Uncertainty," Engineering, Technology & Applied Science Research, vol. 8, no. 2, pp. 2758–2763, Apr. 2018. DOI: https://doi.org/10.48084/etasr.1892
A. Sharifian and S. Sharifian, "A new power system transient stability assessment method based on Type-2 fuzzy neural network estimation," International Journal of Electrical Power & Energy Systems, vol. 64, pp. 71–87, Jan. 2015. DOI: https://doi.org/10.1016/j.ijepes.2014.07.007
S. M. Ashraf, A. Gupta, D. K. Choudhary, and S. Chakrabarti, "Voltage stability monitoring of power systems using reduced network and artificial neural network," International Journal of Electrical Power & Energy Systems, vol. 87, pp. 43–51, May 2017. DOI: https://doi.org/10.1016/j.ijepes.2016.11.008
W. D. Oliveira, J. P. A. Vieira, U. H. Bezerra, D. A. Martins, and B. das G. Rodrigues, "Power system security assessment for multiple contingencies using multiway decision tree," Electric Power Systems Research, vol. 148, pp. 264–272, Jul. 2017. DOI: https://doi.org/10.1016/j.epsr.2017.03.029
R. Zhang, Y. Xu, Z. Y. Dong, K. Meng, and Z. Xu, "Intelligent systems for power system dynamic security assessment: Review and classification," in 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Weihai, China, Jul. 2011, pp. 134–139. DOI: https://doi.org/10.1109/DRPT.2011.5993876
R. Zhang, Y. Xu, Z. Y. Dong, and D. J. Hill, "Feature selection for intelligent stability assessment of power systems," in 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, Jul. 2012. DOI: https://doi.org/10.1109/PESGM.2012.6344780
S. Kalyani and K. S. Swarup, "Pattern analysis and classification for security evaluation in power networks," International Journal of Electrical Power & Energy Systems, vol. 44, no. 1, pp. 547–560, Jan. 2013. DOI: https://doi.org/10.1016/j.ijepes.2012.07.065
B. Xue, M. Zhang, and W. N. Browne, "New fitness functions in binary particle swarm optimisation for feature selection," in 2012 IEEE Congress on Evolutionary Computation, Brisbane, QLD, Australia, Jun. 2012.
J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, WA, Australia, Aug. 1995, vol. 4, pp. 1942–1948.
J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," in Computational Cybernetics and Simulation 1997 IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, USA, Jul. 1997, vol. 5, pp. 4104–4108.
J. D. Glover, M. S. Sarma, and T. Overbye, Power Systems Analysis and Design, 5th ed. 2012.
A. Y. Abdelaziz and M. A. El-Dessouki, "International Journal of Intelligent Systems and Applications(IJISA)," International Journal of Intelligent Systems and Applications(IJISA), vol. 5, no. 10, Sep. 2013, Art. no. 1. DOI: https://doi.org/10.5815/ijisa.2013.10.01
N. Amjady and S. A. Banihashemi, "Transient stability prediction of power systems by a new synchronism status index and hybrid classifier," IET Generation, Transmission & Distribution, vol. 4, no. 4, pp. 509–518, Apr. 2010. DOI: https://doi.org/10.1049/iet-gtd.2009.0255
A. Karami and S. Z. Esmaili, "Transient stability assessment of power systems described with detailed models using neural networks," International Journal of Electrical Power & Energy Systems, vol. 45, no. 1, pp. 279–292, Feb. 2013. DOI: https://doi.org/10.1016/j.ijepes.2012.08.071
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Copyright (c) 2023 Ngoc Au Nguyen, Trong Nghia Le, Hoang Minh Vu Nguyen
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