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基于深度置信网络的电网安全稳定控制系统故障诊断方法 被引量:2

Fault diagnosis method of power grid security and stability control system based on deep belief network
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摘要 安全稳定控制系统(安控系统)控制节点多、控制链条长,若因故障导致拒动或者误动,将给电网的运行造成严重危害。安控系统的故障诊断是电力系统安全稳定运行的基础。现有安控系统的故障诊断主要依赖于技术人员依据通信报文辅助判别异常原因,难以对安控系统各个环节故障进行实时诊断。为此,分析了安控系统故障的存在环节和产生原因,提取了安控系统故障特征量;建立了基于深度置信网络的安控系统故障诊断模型,提出了安控系统故障诊断方法;最后选取安控系统运行故障样本,验证了故障诊断方法的正确性。 The security and stability control system(security control system)has many control nodes and a long control chain.If it refuses to act or malfunction due to a fault,it will cause hazard to the operation of the power grid.The fault diagnosis of the security control system is the basis for the safe and stable operation of the power system.The fault diagnosis of the existing security control system mainly relies on the technical personnel to assist in identifying the abnormal cause according to the communication message,and it is difficult to diagnose the faults of each link of the security control system in real time.To this end,the existing links and causes of faults in safety control system are analyzed,and the feature quantities of faults in safety control system are extracted.And then,a fault diagnosis model of safety control system based on deep belief network is established,and a fault diagnosis method of safety control system is proposed.Finally,the correctness of fault diagnosis method is verified by selecting the operation fault samples of the safety control system.
作者 欧阳金鑫 张澳归 蒋航 熊俊 朱开阳 OUYANG Jinxin;ZHANG Aogui;JIANG Hang;XIONG Jun;ZHU Kaiyang(State Key Laboratory of Power Transmission Equipment&System Security and New Technology(Chongqing University),Chongqing 400044,China;Southwest Branch,State Grid Corporation of China,Chengdu 610094,China;Power Grid Security and Stability Control Technology Branch,State Grid NARI Technology Co.,Ltd.,Nanjing 211106,China)
出处 《电测与仪表》 北大核心 2024年第7期1-6,共6页 Electrical Measurement & Instrumentation
基金 国家电网有限公司总部管理科技项目(SGSXDK00DJJS2100113)。
关键词 电力系统 安控系统 故障诊断 深度置信网络 故障特征量 power system security control system fault diagnosis deep belief network fault feature quantity
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