基于人工智能的电力设施状态监测与故障预警系统研究OA

To address the shortcomings of traditional power facility monitoring methods in terms of real-time performance and accuracy,an AI-based power facility condition monitoring and fault early warning system is designed.Adopting a four-layer architecture of"edge sensing-data transmission-intelligent analysis-application interaction",the system integrates multi-source data collection,edge intelligent processing,hybrid-driven modeling,and distributed storage technologies to achieve real-time monitoring of power equipment status and early fault warnings.The system establishes an expert knowledge base based on knowledge graphs and an intelligent analysis engine utilizing deep learning.Through multi-source heterogeneous data fusion and multi-scale feature extraction,it forms a complete data-to-decision-making chain.Experimental results demonstrate a fault detection rate of 96.8%,an average warning lead time of 6.2 d,and a false alarm rate of only 2.3%,significantly outperforming traditional methods.The system supports multi-level visualization and differentiated information push,enhancing operation and maintenance efficiency and decision-making accuracy.After one year of application,unplanned equipment outages in pilot areas decreased by 42%,annual maintenance costs are reduced by 1.86 million yuan,and the power system's safe operation level and equipment management efficiency are markedly improved.