Intelligent information systems for power grid fault analysis by computer communication technology

Abstract

This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication technology, is proposed and developed. The system incorporates a novel fault diagnosis model, combining advanced communication technologies such as distributed computing, real-time data transmission, cloud computing, and big data analytics, to establish a multi-layered information processing architecture for grid fault analysis. Specifically, this study introduces a fusion model integrating Transformer self-attention mechanisms with graph neural networks (GNNs) based on conventional fault diagnosis techniques. GNNs capture the complex relationships between different nodes within the grid topology, effectively identifying power transmission characteristics and fault propagation paths across grid nodes. The Transformer’s self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. To improve system response speed, edge computing moves portions of fault data preprocessing and analysis to edge nodes near data sources, significantly reducing transmission latency and enhancing real-time diagnosis capability. Experimental results demonstrate that the proposed model achieves superior diagnostic performance across various fault types (e.g., short circuits, overloads, equipment failures) in simulation scenarios. The system achieves a fault identification and location accuracy of 99.2%, an improvement of over 10% compared to traditional methods, with an average response time of 85 milliseconds, approximately 43% faster than existing technologies. Moreover, the system exhibits strong robustness in complex scenarios, with an average fault prediction error rate of just 1.1% across multiple simulations. This study provides a novel solution for intelligent power grid fault diagnosis and management, establishing a technological foundation for smart grid operations.