MRMR Feature Selection Algorithm for Microgrid Frequency Stability Classification
Received: 24 February 2025 | Revised: 2 April 2025 and 7 April 2025 | Accepted: 9 April 2025 | Online: 4 June 2025
Corresponding author: Ngoc Au Nguyen
Abstract
This paper proposes applying the Minimum Redundancy Maximum Relevance (MRMR) algorithm to select variables for constructing a deep neural network-based classifier for Microgrid (MG) frequency stability assessment. The MRMR algorithm is combined with the 1-Nearest Neighbor (1-NN) machine learning classifier (MRMR&1-NN) to evaluate the classification accuracy in feature selection. The study also compares MRMR-based feature selection with Fisher, Relieff, and Chi-squared methods. Reducing the feature space is crucial for minimizing computational cost, optimizing memory usage, and reducing the expenses of sensor measurement equipment in practical applications. Experimental results on a 16-bus MG system demonstrate that the proposed method not only significantly reduces the number of inputs, but also improves the classification accuracy. The MRMR method achieves higher accuracy compared to the other feature selection techniques. Based on the MRMR&1-NN feature selection results, this paper proposes employing a Bidirectional Long Short-Term Memory network with Fully Connected layers (BiLSTM-FC) for model construction. The results indicate that the BiLSTM-FC model achieves high classification accuracy, highlighting the effectiveness of using MRMR for feature selection and applying the BiLSTM-FC classifier for MG frequency stability classification.
Keywords:
feature selection, frenquency stability classification, microgird, neural networks, deep learningDownloads
References
J. S. Ali, Y. Qiblawey, A. Alassi, A. M. Massoud, S. M. Muyeen, and H. Abu-Rub, "Power System Stability With High Penetration of Renewable Energy Sources: Challenges, Assessment, and Mitigation Strategies," IEEE Access, vol. 13, pp. 39912–39934, 2025. DOI: https://doi.org/10.1109/ACCESS.2025.3546491
M. Debouza, A. Al-Durra, T. H. M. EL-Fouly, and H. H. Zeineldin, "Survey on microgrids with flexible boundaries: Strategies, applications, and future trends," Electric Power Systems Research, vol. 205, Apr. 2022, Art. no. 107765. DOI: https://doi.org/10.1016/j.epsr.2021.107765
S. Zarrabian, R. Belkacemi, and A. A. Babalola, "Intelligent mitigation of blackout in real-time microgrids: Neural Network Approach," in 2016 IEEE Power and Energy Conference at Illinois, Urbana, IL, USA, 2016, pp. 1–6. DOI: https://doi.org/10.1109/PECI.2016.7459213
O. A. Alimi, K. Ouahada, and A. M. Abu-Mahfouz, "A Review of Machine Learning Approaches to Power System Security and Stability," IEEE Access, vol. 8, pp. 113512–113531, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3003568
U. Shahzad, "Artificial Neural Network for Transient Stability Assessment: A Review," in 2024 29th International Conference on Automation and Computing, Sunderland, United Kingdom, 2024, pp. 1–7. DOI: https://doi.org/10.1109/ICAC61394.2024.10718782
N. Moarref, S. Jafarzadeh, Y. Yaslan, and V. M. Istemihan Genc, "A Transient Stability Prediction Method based on Multi-Channel Convolutional Neural Networks Using Time Series of PMU Measurements," in 2019 11th International Conference on Electrical and Electronics Engineering, Bursa, Turkey, 2019, pp. 151–155. DOI: https://doi.org/10.23919/ELECO47770.2019.8990476
Z. Li, J. Yan, Y. Liu, W. Liu, L. Li, and H. Qu, "Power system transient voltage vulnerability assessment based on knowledge visualization of CNN," International Journal of Electrical Power & Energy Systems, vol. 155, no. B, Jan. 2024, Art. no. 109576. DOI: https://doi.org/10.1016/j.ijepes.2023.109576
X. Li, C. Liu, P. Guo, S. Liu, and J. Ning, "Deep learning-based transient stability assessment framework for large-scale modern power system," International Journal of Electrical Power & Energy Systems, vol. 139, Jul. 2022, Art. no. 108010. DOI: https://doi.org/10.1016/j.ijepes.2022.108010
T. Yang, B. Li, and Q. Xun, "LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization," IEEE Access, vol. 7, pp. 171471–171484, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2954290
Y. Xu, L. Guan, Z. Y. Dong, and K. P. Wong, "Transient stability assessment on China Southern Power Grid system with an improved pattern discovery-based method," in 2010 International Conference on Power System Technology, Hangzhou, China, 2010, pp. 1–6. DOI: https://doi.org/10.1109/POWERCON.2010.5666045
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
K. S. Swarup, "Artificial neural network using pattern recognition for security assessment and analysis," Neurocomputing, vol. 71, no. 4, pp. 983–998, Jan. 2008. DOI: https://doi.org/10.1016/j.neucom.2007.02.017
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, 2012, pp. 1–7. DOI: https://doi.org/10.1109/PESGM.2012.6344780
N. A. Nguyen, T. N. Le, and H. M. V. Nguyen, "Multi-Goal Feature Selection Function in Binary Particle Swarm Optimization for Power System Stability Classification," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10535–10540, Apr. 2023. DOI: https://doi.org/10.48084/etasr.5799
C. Ding and H. Peng, "Minimum redundancy feature selection from microarray gene expression data," in Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003, Stanford, CA, USA, 2003, pp. 523–528. DOI: https://doi.org/10.1109/CSB.2003.1227396
H. Roubhi, A. H. Gharbi, K. Rouabah, and P. Ravier, "Mutual Information-based Feature Selection Strategy for Speech Emotion Recognition using Machine Learning Algorithms Combined with the Voting Rules Method," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19207–19213, Feb. 2025. DOI: https://doi.org/10.48084/etasr.9066
G. Wang, F. Lauri, and A. H. E. Hassani, "Feature Selection by mRMR Method for Heart Disease Diagnosis," IEEE Access, vol. 10, pp. 100786–100796, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3207492
L. Gong, X. Zhang, T. Chen, and L. Zhang, "Recognition of Disease Genetic Information from Unstructured Text Data Based on BiLSTM-CRF for Molecular Mechanisms," Security and Communication Networks, vol. 2021, no. 1, Feb. 2021, Art. no. 6635027. DOI: https://doi.org/10.1155/2021/6635027
Z. Zhao, P. Yang, J. M. Guerrero, Z. Xu, and T. C. Green, "Multiple-Time-Scales Hierarchical Frequency Stability Control Strategy of Medium-Voltage Isolated Microgrid," IEEE Transactions on Power Electronics, vol. 31, no. 8, pp. 5974–5991, Aug. 2016. DOI: https://doi.org/10.1109/TPEL.2015.2496869
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Copyright (c) 2025 Ngoc Au Nguyen, Nghia Le Trong, Duy Bui Cong, Phuong Nam Nguyen
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