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

Lakshin Pathak; Khushi Vasava; Stuti Gulati; Shreya Bhatia1

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Publication Date: 2025/01/03

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.

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

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24DEC745.pdf

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