Deep Neural Network-Based Smart Grid Stability Analysis: Enhancing Grid Resilience and Performance

Pranobjyoti Lahon, Aditya Bihar Kandali, Utpal Barman, Ruhit Jyoti Konwar, Debdeep Saha and Manob Jyoti Saikia ()
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Pranobjyoti Lahon: Department of Electrical Engineering, Assam Science and Technology University, Guwahati 781014, India
Aditya Bihar Kandali: Department of Electrical Engineering, Jorhat Engineering College, Jorhat 785007, India
Utpal Barman: Faculty of Computer Technology, Assam Down Town University, Guwahati 781026, India
Ruhit Jyoti Konwar: Faculty of Engineering, Assam Down Town University, Guwahati 781026, India
Debdeep Saha: Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Manob Jyoti Saikia: Department of Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USA

Energies, 2024, vol. 17, issue 11, 1-17

Abstract: With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the stability and reliability of smart grids. This study explores the performance of various machine learning classifiers in predicting the stability of smart grid systems. Utilizing a smart grid dataset obtained from the University of California’s machine learning repository, classifiers such as logistic regression (LR), XGBoost, linear support vector machine (Linear SVM), and SVM with radial basis function (SVM-RBF) were evaluated. Evaluation metrics, including accuracy, precision, recall, and F1 score, were employed to assess classifier performance. The results demonstrate high accuracy across all models, with the Deep Neural Network (DNN) model achieving the highest accuracy of 99.5%. Additionally, LR, linear SVM, and SVM-RBF exhibited comparable accuracy levels of 98.9%, highlighting their efficacy in smart grid stability prediction. These findings underscore the utility of machine learning techniques in enhancing the reliability and efficiency of smart grid systems.

Keywords: smart grid; machine learning; deep learning; stability; reliability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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