EFFICIENT ARTIFICIAL NEURAL NETWORKS FOR ENHANCING SMART GRID STABILITY: A COMPREHENSIVE REVIEW OF TECHNIQUES AND APPLICATIONS
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.4826Keywords:
Smart Grids, Artificial Neural Networks, Renewable Energy Integration, Predictive Analytics, Real-Time Control, Grid StabilityAbstract [English]
Renewable energy sources and better control systems are what make smart grids unique. To keep them stable and efficient, they need strong and flexible solutions. Artificial Neural Networks (ANNs) have become very important in solving these problems because they can make smart predictions and decisions in real time, which is very important for smart grids. This in-depth review looks at different ANN designs and how they can be used to make smart grids more stable. First, we talk about the things that make smart grids different, like spread production and changing load needs, which make security more difficult. Then, we talk about how ANNs can help predict demand and green output, focussing on how they can change to changing grid conditions and learn from data that isn't straight and has a lot of dimensions. The review talks about a few important techniques, such as deep learning and reinforcement learning, that have shown a lot of promise in predictive analytics and real-time control tasks. We also look at examples of how ANNs have been used successfully to predict problems, control energy flow, and improve grid operations, showing real improvements in the stability and efficiency of the grid. It also talks about the problems with integrating ANN solutions and the way forward for using them on a larger scale in smart grids, with a focus on privacy, security, and interoperability. This review combines recent research and real-world applications to give a basic knowledge and encourage more new ideas in this area that is changingquickl.
References
Deepa, N.; Pham, Q.-V.; Nguyen, D.C.; Bhattacharya, S.; Prabadevi, B.; Gadekallu, T.R.; Maddikunta, P.K.R.; Fang, F.; Pathirana, P.N. A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions. Future Gener. Comput. Syst. 2022, 131, 209–226. DOI: https://doi.org/10.1016/j.future.2022.01.017
Liu, H.; Lang, B. Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Appl. Sci. 2019, 9, 4396. DOI: https://doi.org/10.3390/app9204396
Barman, U.; Choudhury, R.D. Smartphone Assist Deep Neural Network to Detect the Citrus Diseases in Agri-Informatics. Glob. Transit. Proc. 2022, 3, 392–398. DOI: https://doi.org/10.1016/j.gltp.2021.10.004
Barman, U.; Barman, V.; Rahman, M.; Choudhury, N.K. Graph Based Extractive News Articles Summarization Approach Leveraging Static Word Embeddings. In Proceedings of the 2021 International Conference on Computational Performance Evaluation (ComPE), Shillong, India, 1–3 December 2021; pp. 8–11. DOI: https://doi.org/10.1109/ComPE53109.2021.9752056
Dutta, D.; Choudhury, R.D.; Barman, U. Assamese Speech-Based Vocabulary Identification System Using Convolutional Neural Network. Int. J. Comput. Digit. Syst. 2022, 12, 1191–1202. DOI: https://doi.org/10.12785/ijcds/120195
Muralitharan, K.; Sakthivel, R.; Vishnuvarthan, R. Neural Network Based Optimization Approach for Energy Demand Prediction in Smart Grid. Neurocomputing 2018, 273, 199–208. DOI: https://doi.org/10.1016/j.neucom.2017.08.017
Wang, H.; Zhang, Y.-M.; Mao, J.-X. Sparse Gaussian Process Regression for Multi-Step Ahead Forecasting of Wind Gusts Combining Numerical Weather Predictions and on-Site Measurements. J. Wind Eng. Ind. Aerodyn. 2022, 220, 104873. DOI: https://doi.org/10.1016/j.jweia.2021.104873
Ghosh, A.; Kole, A. A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction. TechRxiv. 2021. DOI: https://doi.org/10.36227/techrxiv.16863145.v1
Alquthami, T.; Zulfiqar, M.; Kamran, M.; Milyani, A.H.; Rasheed, M.B. A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid. IEEE Access 2022, 10, 48419–48433. DOI: https://doi.org/10.1109/ACCESS.2022.3171270
Paraschiv, L.S.; Paraschiv, S. Contribution of renewable energy (hydro, wind, solar and biomass) to decarbonization and transformation of the electricity generation sector for sustainable development. Energy Rep. 2023, 9, 535–544. DOI: https://doi.org/10.1016/j.egyr.2023.07.024
Khalid, M. Smart grids and renewable energy systems: Perspectives and grid integration challenges. Energy Strategy Rev. 2024, 51, 101299. DOI: https://doi.org/10.1016/j.esr.2024.101299
Babayomi, O.; Zhang, Z.; Dragicevic, T.; Hu, J.; Rodriguez, J. Smart grid evolution: Predictive control of distributed energy resources—A review. Int. J. Electr. Power Energy Syst. 2023, 147, 108812. DOI: https://doi.org/10.1016/j.ijepes.2022.108812
Ohanu, C.P.; Rufai, S.A.; Oluchi, U.C. A comprehensive review of recent developments in smart grid through renewable energy resources integration. Heliyon 2024, 10, e25705. DOI: https://doi.org/10.1016/j.heliyon.2024.e25705
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Kazi Kalim Nasir , Dr. Jitendra N Shinde, Dr. Raju M Sairise
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
