摘要
换流站阀冷系统的异常有时被掩盖在正常监控信号的波动中而难以识别。同时,一些极端工况下冷却能力是否充裕却难以评估。从大数据应用的角度,存在数据源少、信息维数低的问题。与之对应的算法模型逻辑简单,人工智能技术应用不充分。为解决上述问题,提出了基于多源数据的换流阀冷却能力建模与预警方法。在分析阀冷系统运行原理及多种状态检测手段的基础上,对阀冷却能力的定义进行了量化,建立了换流阀多维度冷却能力评价模型。基于多维度时序趋势分析和相关性分析算法,建立了阀冷系统缺陷的预警模型。在穗东站应用上述模型分析了阀冷系统监控数据,验证了模型的有效性。
The abnormality of valve cooling system in converter station is sometimes concealed in the fluctuation of normal monitoring signal and difficult to be identified.The adequacy of cooling capacity under some extreme conditions is difficult to assess.There are some problems such as few data sources and low information dimension,from the point of view of big data application.The corresponding algorithm model has simple logic and insufficient application of artificial intelligence technology.To solve the above problems,a cooling capacity modeling and early warning method for converter valves based on multi-source data is proposed.Based on the analysis of the operation principle of valve cooling system and the analysis of various state detection methods,the definition of cooling capacity is quantified and a multi-dimensional evaluation model is established.Based on multi-dimensional time series trend analysis and correlation analysis algorithm,a defect early warning model of valve cooling system is established.The validity of the model is verified by applying the model to the analysis of the monitoring data of valve cooling system at Suidong Station.
作者
廖毅
罗炜
蒋峰伟
李亚锦
于大洋
LIAO Yi;LUO Wei;JIANG Fengwei;LI Yajin;YU Dayang(Guangzhou Bureau of EHV Power Transmission Company,CSG,Guangzhou 510405,China;China Southern Power Grid Co.,Ltd.,Guangzhou 510663,China;School of Electrical Engineering,Shandong University,Jinan 250061,China)
出处
《南方电网技术》
CSCD
北大核心
2020年第7期1-9,共9页
Southern Power System Technology
关键词
阀冷系统
多源数据
冷却能力
时序趋势分析
valve cooling system
multi-source data
cooling capacity
time series trend analysis
