Real-Time Stability Analysis of Smart Grids Using Deep Neural Networks


Authors : Lakshin Pathak; Khushi Vasava; Stuti Gulati; Shreya Bhatia

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/y358v6s6

Scribd : https://tinyurl.com/yc6t4htp

DOI : https://doi.org/10.5281/zenodo.14576727

Abstract : This paper explores the application of deep learning techniques to predict smart grid stability. With the growing adop- tion of renewable energy sources, the unpredictability of energy supply and fluctuating consumer demands pose challenges to grid stability. The proposed framework utilizes Artificial Neural Networks (ANNs) to analyze operational parameters, such as power values and time constants, for classifying grid conditions as stable or unstable. The dataset is preprocessed with normalization techniques and trained using a feed- forward neural network with ReLU and sigmoid activation functions, optimized with the Adam optimizer. The framework achieves high accuracy and robustness, as demonstrated by cross-validation and performance metrics like precision, recall, and F1-score. The results highlight the potential of deep learning to enhance grid reliability and support real-time decision-making. This study contributes to the integration of AI technologies in energy systems, ensuring efficient management and sustainable use of renewable energy resources.

Keywords : Smart Grid, Deep Learning, Stability Predic- tion, Power Systems, Neural Networks.

References :

  1. Bashir, Ali Kashif, et al.” Comparative analysis of machine learning algorithms for prediction of smart grid stability.” International Transac- tions on Electrical Energy Systems 31.9 (2021): e12706.
  2. Wang, X., et al.” Machine Learning in Power Systems: Stability Predic- tion.” IEEE Transactions on Power Systems, 2020.
  3. Zhou, Y., et al.” Deep Learning for Renewable Energy Integration in Smart Grids.” Energy, 2021.
  4. Li, Z., et al.” Artificial Intelligence in Smart Grid Management.” IEEE Transactions on Smart Grid, 2020.
  5. Dimitrijevic´, Marko A., et al.” Implementation of artificial neural networks based AI concepts to the smart grid.” Facta Universitatis, Series: Electronics and Energetics 27.3 (2014): 411-424.
  6. Omitaomu, Olufemi A., and Haoran Niu.” Artificial intelligence tech- niques in smart grid: A survey.” Smart Cities 4.2 (2021): 548-568.
  7. Shi, Zhongtuo, et al.” Artificial intelligence techniques for stability anal- ysis and control in smart grids: Methodologies, applications, challenges and future directions.” Applied Energy 278 (2020): 115733.
  8. Yan, J., et al.” Machine learning for the prediction of smart grid stability: A review.” International Journal of Electrical Power and Energy Systems 124 (2021): 106325.

This paper explores the application of deep learning techniques to predict smart grid stability. With the growing adop- tion of renewable energy sources, the unpredictability of energy supply and fluctuating consumer demands pose challenges to grid stability. The proposed framework utilizes Artificial Neural Networks (ANNs) to analyze operational parameters, such as power values and time constants, for classifying grid conditions as stable or unstable. The dataset is preprocessed with normalization techniques and trained using a feed- forward neural network with ReLU and sigmoid activation functions, optimized with the Adam optimizer. The framework achieves high accuracy and robustness, as demonstrated by cross-validation and performance metrics like precision, recall, and F1-score. The results highlight the potential of deep learning to enhance grid reliability and support real-time decision-making. This study contributes to the integration of AI technologies in energy systems, ensuring efficient management and sustainable use of renewable energy resources.

Keywords : Smart Grid, Deep Learning, Stability Predic- tion, Power Systems, Neural Networks.

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