DOI : https://doi.org/10.18517/ijaseit.14.5.20450

Enhancing Smart Grid Stability through a Hybrid Biometric Pattern Recognition and Long Short-Term Memory Approach

Mukhiddinov Muzrob Orif Uglia (1), Yong Min Kim (2), YeonJae Oh (3), Chang-Gyoon Lim (4)
(1) Department of Computer Engineering, Chonnam National University,50 Daehak Ro, Yeosu, 559626, Republic of Korea
(2) Department of Electronic Commerce, Chonnam National University,50 Daehak Ro, Yeosu, 559626, Republic of Korea
(3) Department of Cultural Contents, Chonnam National University, 50 Daehak Ro, Yeosu, 559626, Republic of Korea
(4) Department of Computer Engineering, Chonnam National University,50 Daehak Ro, Yeosu, 559626, Republic of Korea
Fulltext View | Download
How to cite (IJASEIT) :
Uglia, Mukhiddinov Muzrob Orif, et al. “Enhancing Smart Grid Stability through a Hybrid Biometric Pattern Recognition and Long Short-Term Memory Approach”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 5, Oct. 2024, pp. 1619-25, doi:10.18517/ijaseit.14.5.20450.
Citation Format :
The stability of power grids is critical in ensuring the consistent and efficient delivery of electricity. However, traditional predictive models often fall short in addressing grid data's intricate and ever-changing nature, making it challenging to maintain grid reliability. This paper introduces a novel hybrid approach that combines Biometric Pattern Recognition (BPR) with Long Short-Term Memory (LSTM) networks to enhance the prediction of smart grid stability. This approach employs BPR techniques to extract essential features from smart grid data by leveraging the pattern recognition capabilities typically used in biometric systems. These techniques are particularly effective in identifying and isolating the most relevant patterns within the complex datasets generated by smart grids. On the other hand, the LSTM-based model is designed to handle the temporal dependencies and nonlinear patterns characteristic of grid data. LSTMs, known for their proficiency in time-series analysis, are well-suited for capturing the sequential nature of grid data, enabling more accurate predictions over time. Integrating BPR and LSTM in this hybrid model addresses several limitations in existing predictive methods. By combining the strengths of both techniques, the model enhances the accuracy of predictions and improves the overall reliability of grid stability assessments. Extensive experiments were conducted using real-world datasets to validate the effectiveness of the proposed hybrid model. The results demonstrate a significant improvement in prediction accuracy, with the BPR-LSTM model achieving a 98.25% increase in accuracy compared to traditional prediction methods. This improvement underscores the potential of the BPR-LSTM hybrid approach to play a pivotal role in advancing the stability and reliability of smart grid systems.

J. Joo and J. Oh, “Efficient Grid-Independent ESS control System by Prediction of Energy Production Consumption,” J. of the Korea Institute of Electronic Communication Sciences, vol. 14 no. 1, 2020, pp.155-160. https://doi.org/10.13067/JKIECS.2019.14.1.155.

Y. Mi, “Renewable Energy Generation Prediction Model using Meteorological Big Data,” J. of the Korea Institute of Electronic Communication Sciences, vol. 18 no. 01, 2023, pp.39-44. https://doi.org/10.13067/JKIECS.2023.18.1.39.

P. Glauner, “Artificial Intelligence for the Detection of Electricity Theft and Irregular Power Usage in Emerging Markets,” Dissertation, University of Luxembourg, 2019.

B. Schäfer, C. Grabow, S. Auer, J. Kurths, D. Witthaut, and M. Timme, “Taming Instabilities in Power Grid Networks by Decentralized Control,” European Physical Journal Special Topics, vol. 225, no. 1, Aug. 2016, pp. 569–582. https://doi.org/10.48550/arXiv.1508.02217.

D. Solyali, “A comparative analysis of machine learning approaches for short-/long-term electricity load forecasting in Cyprus,” Sustainability (Switzerland), vol. 12, no. 9, May 2020, pp. 3612-3634. https://doi.org/10.3390/su12093612.

Majed A. Alotaibi, “Machine Learning Approach for Short-Term Load Forecasting Using Deep Neural Network,” Energies 2022, 15(17), pp. 1-23. https://doi.org/10.3390/en15176261

Salava V Satyanarayana1 and Pillalamarri Madhavi, “Machine, “Learning for Load Forecasting in Power Systems,” E3S Web of Conferences 453, 2023, pp. 1-6. https://doi.org/10.1051/e3sconf/202345301008

Jian RONG, Xiaohua LIU, Kunyang GAO, “Application of AI Algorithms in Power System Load Forecasting Under the New Situation, “Advances in Artificial Intelligence, Big Data and Algorithms, IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0, 2023, pp. 65-74. doi:10.3233/FAIA230793

S. Liu, “A data-driven and data-based framework for online voltage stability assessment using partial mutual information and iterated random forest,” Energies (Basel), vol. 14, no. 3, Feb. 2021, pp. 715-731. https://doi.org/10.3390/en14030715.

Mohamed Mohamed Khaleel, Salah Ali Abulifa, and Adel Ali Abulifa, “Artificial Intelligent Techniques for Identifying the Cause of Disturbances in the Power Grid,” Brilliance: Research of Artificial Intelligence, Volume 3, Number 1, May 2023, pp. 19-31. https://doi.org/10.47709/brilliance.v3i1.2165

A. Asif, A. Ijaz,A. Urooj, T. Khan and A. Munir, "Voltage Collapse Prediction Using Artificial Neural Network," Journals Engineering Proceedings Volume 46 Issue 1, 2023, pp. 1-7. https://doi.org/10.3390/engproc2023046024.

Vishnu Murthy K. and and Ashok Kumar L, “Power quality analysis in electrical drives and a case study of artificial intelligence prediction algorithm for fault deterrent electrical drives,” BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES, Vol. 70(3), 2022, pp. 1-8. DOI: 10.24425/bpasts.2022.141180

Z. Shi, “Convolutional neural network-based power system transient stability assessment and instability mode prediction,” Appl Energy, vol. 263, Apr. 2020, pp. 123-142. https://doi.org/10.1016/j.apenergy.2020.114586

P. Sarajcev, A. Kunac, G. Petrovic and M. Despalatovic, “Artificial Intelligence Techniques for Power System Transient Stability Assessment,” Energies 15, 507, 2022, pp. 1-22. https://doi.org/10.3390/en15020507

W. Guo, N. Qureshi, M. Jarwar, J. Kim, and D. Shin, “AI-Oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities,” Sustainable Energy Technologies and Assessments 56, January 2023, pp 1-22. DOI:10.1016/j.seta.2022.102990

H. Lee, J. Kim, J. Ho Park and S. Chung, “Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map,” Energies, 16(23), 2023, pp. 1-22. https://doi.org/10.3390/en16237743.

Q. Wang, F. Li, Y. Tang, and Y. Xu, “Integrating Model-Driven and Data-Driven Methods for Power System Frequency Stability Assessment and Control,” IEEE Transactions on Power Systems, vol. 34, no. 6, Nov. 2019, pp. 4557–4568. DOI: 10.1109/TPWRS.2019.2919522.

J. Li, Sheng Chen, X. Wang, and T. Pu, “Load Shedding Control Strategy in Power Grid Emergency State Based on Deep Reinforcement Learning,” CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, VOL. 8, NO. 4, JULY 2022, pp. 1175-1182. DOI: 10.17775/CSEEJPES.2020.06120

S. Sundarajoo, and D. M. Soomro, “Optimal Load Shedding for Voltage Collapse Prevention Following Overloads in Distribution System,” IJE TRANSACTIONS A: Basics Vol. 36 No. 07, July 2023, pp. 1230-1238. doi: 10.5829/ije.2023.36.07a.04

Y. Zhao, S. You, M. Mandich, L. Zhu, C. Zhang, H. Li, Y. Su, C. Zeng, Y. Zhao, Y. Liu, H. Jiang, H. Yuan, Y. Zhang and J. Tan, “Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids,” Energies 14, 6563, 2021, pp. 1-17. https://doi.org/10.3390/en14206563

A. Sujan Reddy, S. Akashdeep, R. Harshvardhan, S. Kamath, “Stacking Deep learning and Machine learning models for short-term energy consumption forecasting,” Advanced Engineering Informatics, Volume 52, April 2022, pp. 534-546. https://doi.org/10.1016/j.aei.2022.101542.

L. Xi, J. Wu, Y. Xu, and H. Sun, “Automatic Generation Control Based on Multiple Neural Networks with Actor-Critic Strategy,” IEEE Trans Neural New Learn Syst, vol. 32, no. 6, Jun. 2021, pp. 2483–2493. DOI: 10.1109/TNNLS.2020.3006080.

Mustaffa, Zuriani, et al. “Time Series Predictive Analysis Based on Hybridization of Meta-Heuristic Algorithms,” International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 5, Oct. 2018, pp. 1919-25. DOI:10.18517/ijaseit.8.5.4968.

S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, 1997, pp. 1735-1780. DOI: 10.1162/neco.1997.9.8.1735

E. Kwak and C. Moon, “Analysis of Power System Stability by Deployment of Renewable Energy Resources,” J. of the Korea Institute of Electronic Communication Sciences, vol. 16 no. 4, 2021, pp.633-642. https://doi.org/10.13067/JKIECS.2021.16.4.633

A. K. Jain, A. Ross, and S. Prabhakar, "An Introduction to Biometric Recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, Jan. 2004, pp. 4-20. DOI: 10.1109/TCSVT.2003.818349

S. Hesaraki, “Long Short-Term Memory”, Medium.com, Oct. 2023, [Online], Available: https://medium.com/@saba99/long-short-term-memory-lstm-fffc5eaebfdc. [Accessed: Jan. 2024].

H. Lee, I. Na, K. Bultakov, and Y. Kim, "A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors," Computers and Materials & Continua, vol. 71, no. 03, Nov. 2022, pp. 5413-5425. https://doi.org/10.32604/cmc.2022.023172

C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, Sep. 1995, pp. 273-297.

L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, Oct. 2001, pp. 5-32. https://doi.org/10.1023/A:1010933404324

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, vol. 86, no. 11, Nov. 1998, pp. 2278-2324. DOI: 10.1109/5.726791

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning Representations by Back-Propagating Errors," Nature, vol. 323, no. 6088, Oct. 1986, pp. 533-536. J. Joo and J. Oh, “Efficient Grid-Independent ESS control System by Prediction of Energy Production Consumption,” J. of the Korea Institute of Electronic Communication Sciences, vol. 14 no. 1, 2020, pp.155-160. DOI: 10.1109/5.726791

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).