Enhancing Smart Grid Reliability: Fault Detection in Phasor Measurement Unit Images With Deep Learning

Stable operations of modern power networks depend on the reliability of smart grids. The expansion of modern power systems combined with renewable energy applications and decentralized networks creates difficulties for traditional fault detection methods because of high-dimensional data combined with measurement noise and delayed fault identification processes. A deep learning solution based on Phasor Measurement Unit (PMU) image data detection for smart grid faults is presented in this work. The proposed method achieves accurate and efficient fault classification through the conversion of PMU data to images and then utilization of advanced deep learning models VGG16 combined with Convolutional Neural Networks (CNNs). The experimental findings show that VGG16 reaches 98.75% accuracy in fault classification while surpassing CNNs that delivered 94.44% accuracy. In addition to reporting high classification accuracy, the study proposes a new hybrid deep learning framework that combines transfer learning and adaptive thresholding techniques. The image-based deep learning system extracts features automatically, reducing the need for human preprocessing to effectively detect fault patterns. The methodology enables immediate fault detection, allowing operators to make critical decisions to avoid major power outages. According to the research, artificial intelligence has the potential to significantly improve smart grid resilience, reduce economic losses, and improve power system operational efficiency. The research advances AI developments in energy management by developing advanced, powerful fault detection methods that will guide future changes to power system networks.