基于产业链的电力设备智能故障诊断方法研究

Research on Construction and Application of Grid Equipment Fault Diagnosis Based on Industrial Chain

电力设备故障是导致电网无法正常运行的重要原因。针对电力设备故障诊断机制因缺乏协同管理和知识共享导致的效率低下和技术更新受限问题,提出一种基于产业链的电力设备故障诊断方法。首先基于设备故障诊断历史数据,采用BERT-BiLSTM-ATT深度神经网络构建设备故障知识图谱;然后从上下游协同、虚实互补两个方面构建电力设备故障诊断跨时空信息联动模式。该方法解决了电力设备故障根源追溯、处理措施检索难题,有助于推动电力设备的智能化、可视化故障诊断。

Power equipment failures are an important reason for the inability of the power grid to operate normally.A power equipment fault diagnosis method based on industry chain concept is proposed to address the low efficiency and limited technological updates caused by the lack of collaborative management and knowledge sharing in the power equipment fault diagnosis mechanism.The study aims to construct a fault knowledge graph,update management models,and construct a cross temporal and spatial information linkage model for power equipment fault diagnosis from two aspects:upstream and downstream collaboration,virtual and real complementarity,to solve the problems of tracing the root cause of power equipment faults,retrieving treatment measures,and sharing historical defects among multiple parties.Research is significant in promoting the intelligence and visualization of fault diagnosis in power equipment.