摘要
针对传统变电站运维模式存在的响应滞后、资源利用率低等问题,提出了一种融合多模态数据与混合智能算法的预测性维护框架。通过构建“端-边-云”协同的电力大数据平台,集成设备电气参量、机械振动、绝缘状态及环境参数等多维度数据流,设计基于张量分解的特征降维方法与时空图卷积网络(ST-GCN),突破设备状态监测的时空关联建模瓶颈。创新性地将物理仿真数据与实时监测信息融合,建立数字孪生增强型预测模型,并开发具备环境自适应能力的动态权重调整机制。在华南某220 kV智能变电站的实证应用中,该模型实现变压器绕组过热预警准确率为92.7%、断路器机械故障识别率为89.3%,较传统方法分别提升了31%和45%。经济效益分析表明,预测性维护体系使设备平均故障修复时间缩短至4.3 h,年度运维成本降低了38.6%,设备寿命周期延长2.8年。不仅为智能变电站提供了一套可扩展的运维决策支持系统,还揭示了多物理场耦合作用下的设备退化机理,为电力系统数字化转型提供了理论支撑与技术范式。
In response to the problems of response lag and low resource utilization in the traditional substation operation and maintenance mode,this study proposes a predictive maintenance framework that integrates multimodal data and hybrid intelligent algorithms.By building a collaborative"end edge cloud"power big data platform that integrates multi-dimensional data streams such as equipment electrical parameters,mechanical vibration,insulation status,and environmental parameters,a feature dimensionality reduction method based on tensor decomposition and a spatiotemporal graph convolutional network(ST-GCN)are designed to break through the bottleneck of spatiotemporal correlation modeling in equipment state monitoring.Innovatively integrating physical simulation data with real-time monitoring information,establishing a digital twin enhanced prediction model,and developing a dynamic weight adjustment mechanism with environmental adaptability.In the empirical application of a 220kV intelligent substation in South China,the model achieved an accuracy rate of 92.7%for transformer winding overheating warning and 89.3%for circuit breaker mechanical fault recognition,which were 31%and 45%higher than traditional methods,respectively.Economic benefit analysis shows that the predictive maintenance system reduces the average fault repair time of equipment to 4.3 hours,reduces annual operation and maintenance costs by 38.6%,and extends equipment life cycle by 2.8 years.This study not only provides a scalable operation and maintenance decision support system for intelligent substations,but also reveals the mechanism of equipment degradation under the coupling of multiple physical fields,providing theoretical support and technical paradigms for the digital transformation of power systems.
作者
任思成
邵鹤明
石磊
REN Sicheng;SHAO Heming;SHI Lei(State Grid Jiangsu Electric Power Co.,Ltd.Suzhou Power Supply Branch,Suzhou 215000,China)
出处
《电工技术》
2025年第S1期251-253,256,共4页
Electric Engineering
关键词
大数据分析
智能运维
数字孪生
多模态融合
预测性维护
big data analysis
intelligent operation and maintenance
digital twin
multimodal fusion
predictive maintenance
