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Articles

Vol. 1 No. 1 (2026): July 2026

Optimization of Power Grid Fault Detection Using Convolutional Neural Networks (CNN) in Smart Grid Systems: A Systematic Literature Review

Submitted
July 2, 2026
Published
2026-07-03

Abstract

The rapid evolution of smart grid infrastructure has created an urgent need for highly accurate and efficient fault detection mechanisms that can operate in real-time across complex, interconnected power networks. Conventional fault detection approaches including relay-based systems, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) have demonstrated limitations in terms of detection accuracy, adaptability to non-stationary signal characteristics, and robustness under noisy operational conditions. This paper presents a Systematic Literature Review (SLR) examining the application of Convolutional Neural Networks (CNN) as an advanced solution for optimizing fault detection in smart grid systems. Following the PRISMA 2020 framework, a comprehensive search across Scopus, IEEE Xplore, ScienceDirect, and Web of Science databases yielded 1,247 initial records, of which 86 peer-reviewed studies published between 2020 and 2025 met the final inclusion criteria. The synthesis of reviewed literature demonstrates that CNN-based models achieve superior detection accuracy consistently ranging from 98.5% to 99.99% compared to conventional SVM models (92–97%) and standard ANN approaches (94–99%). Key findings reveal that CNN architectures, particularly hybrid CNN-LSTM and 2D-CNN variants utilizing wavelet-based scalogram representations, effectively extract both temporal and spatial features from fault signals. The review identifies critical research gaps including limited deployment in real-world heterogeneous grid topologies, data imbalance challenges, and the computational overhead of deep architectures for edge deployment. These findings establish a clear foundation for future development of lightweight, optimized CNN models tailored for real-time smart grid fault detection.

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