Knowledge graph-based multimodal neural networks for smart-grid defect detection

The application of artificial intelligence in smart grid operations, particularly for defect detection, has seen remarkable progress. However, effectively leveraging multi-modal data sources to enhance defect detection performance remains challenging. This paper presents a novel Knowledge Graph-based Multimodal Neural Network (KGMNN) approach for robust and efficient defect detection in smart grids. Our proposed method first leverages multiple heterogeneous data sources, such as structural data, temporal sensor readings, and maintenance records, creating a comprehensive representation of the grid system. The system is then modeled as a knowledge graph, with nodes representing various entities and edges signifying their interconnections and interactions. This paper introduces a novel neural network architecture that effectively leverages the knowledge graph structure. This architecture incorporates multi-modal fusion techniques, enabling the model to process and integrate diverse data sources. It can recognize intricate patterns indicative of potential defects that are often undetectable in individual data modes. Experimental results on several real-world smart grid datasets demonstrate that the KGMNN significantly outperforms state-of-the-art techniques in terms of precision, recall, and computational efficiency. Furthermore, the KGMNN's ability to provide explainable predictions, aided by the inherent interpretability of the knowledge graph structure, is of significant practical value in the field. This research paves the way for a new generation of AI-powered defect detection in smart grids, facilitating enhanced grid reliability and operational efficiency.