摘要
针对低压集抄系统故障形式多样复杂、当前运维水平难以满足日益上升的用户需求的问题,提出了一种融合拓扑解析及深度学习的低压集抄系统故障诊断方法。从规划和运行两个阶段出发,分析变压器-集中器、集中器-电能表关联关系,对低压集抄系统拓扑结构进行解析。结合确定的物理拓扑及信息流动路径,基于深度学习理论,通过对涌现故障事件离线学习自动建立基于深度置信网络的故障诊断模型。根据在线获取的系统关键运行特征,建立系统故障断面特征向量,通过训练好的系统诊断模型获得最终诊断结果。算例结果表明,方法能有效准确地实现低压集抄系统故障诊断,能有效应对故障特征信息遗漏和错误的情况。
Aiming at the problems that the faults in low-voltage centralized meter reading system are complex and the current operation and maintenance level is difficult to meet the harsh user demand,we proposed a fault diagnosis method for LV centralized meter reading system based on topology analysis and deep learning.Starting from the two stages of planning and operation,we analyzed the transformer-concentrator association and concentrator-electric energy meter association to diagnose the physical topology of LV centralized meter reading system.Based on the determined physical topology and information flow path,a deep belief network fault diagnosis model is automatically established by offline learning with emerging fault events.According to the online obtaining of the vital systematic operation character,the system fault section feature vector is established and sent to the well-trained fault diagnosis model for final diagnosis result.The result of the case study have showed that the proposed method can effectively and accurately diagnose the fault in LV centralized meter reading system,and it’s effective to deal with the case of the missing information and wrong information.
作者
罗步升
林志超
何小龙
Luo Busheng;Lin Zhichao;He Xiaolong(Huizhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Huizhou 516001,Guangdong,China)
出处
《电测与仪表》
北大核心
2019年第20期145-152,共8页
Electrical Measurement & Instrumentation
基金
广东电网有限责任公司科技项目(031300KK52160-024)
国家自然科学基金项目(51577073)
关键词
低压集抄系统
拓扑解析
深度学习
深度置信网络
故障诊断
LV centralized meter reading system
topology analysis
deep learning
deep belief network
fault diagnosis
