Intelligent Fault Diagnosis in Smart Grids: Leveraging PMU Data with VGG-Based CNN Models

The evolution of smart grid technology necessitates sophisticated methods for fault detection to ensure system reliability and efficiency. Monitoring a complex power grid with phasor measurement units (PMUs) continuously transmitting data at high velocities. Rapidly and accurately analyzing this extensive data to detect faults presents a major challenge for grid operators. This study introduces a novel approach for fault classification in smart grids by utilizing Convolutional Neural Networks (CNNs) based on the architectures of VGG16 and VGG19. VGG is capable of rapidly classifying various grid events, such as faults, generation losses, and synchronous motor switching, with high efficiency. The system detects faults swiftly, allowing operators to minimize downtime and prevent significant damage by enabling prompt responses. The study meticulously examines the performance metrics of each model, including accuracy, precision, recall, and F1 score. Evaluations reveal that the VGG16 model outperforms VGG19, achieving an impressive accuracy of 98.75 % and consistent precision, recall, and F1 scores of 0.99. In contrast, the VGG19 model attained a lower accuracy of 95.00 %, with slightly diminished performance metrics. These findings highlight the efficacy of advanced deep learning techniques in improving fault detection accuracy within smart grid systems, suggesting that VGG16 offers a more reliable and accurate solution compared to VGG19.