基于深度学习的智能录波器配置数据自动化映射方法

李铁成; 任江波; 刘清泉; 耿少博; 王志华; 周达明

李铁成,任江波,刘清泉,耿少博,王志华,周达明.基于深度学习的智能录波器配置数据自动化映射方法[J].电测与仪表,2022,59(9):76-83.
Li Tiecheng,Ren Jiangbo,Liu Qingquan,Gen Shaobo,Wang Zhihu,Zhou Daming.Automatic mapping method of intelligent recorder configuration datasets based on deep learning[J].Electrical Measurement & Instrumentation,2022,59(9):76-83.

基于深度学习的智能录波器配置数据自动化映射方法

Automatic mapping method of intelligent recorder configuration datasets based on deep learning

DOI：10.19753/j.issn1001-1390.2002.09.011

中文关键词: 智能变电站文本挖掘全站配置描述文件智能二次设备动态卷积神经网络

英文关键词:intelligent substation, text mining, substation configuration description, intelligent secondary device, DCNN

基金项目:

作者中文名	作者英文名	单位
李铁成	Li Tiecheng	国网河北省电力有限公司电力科学研究院，石家庄 050021
任江波	Ren Jiangbo	国网河北省电力有限公司，石家庄 050021
刘清泉	Liu Qingquan	国网河北省电力有限公司电力科学研究院，石家庄 050021
耿少博	Gen Shaobo	国网河北省电力有限公司，石家庄 050021
王志华	Wang Zhihu	武汉凯默电气有限公司，武汉 430023
周达明	Zhou Daming	武汉凯默电气有限公司，武汉 430023

摘要点击次数: 2334

中文摘要:

智能变电站配置描述文件中包含大量智能二次设备数据输出接口地址的配置数据集，将这些数据集映射至智能录波器各信息组是保证录波器精准采集设备运行数据的基础性步骤，当前主流映射方法是依照输出接口描述文本人工映射对应的配置数据，二次设备数目繁多时映射工作量大，而描述文本一定程度的不规范性给数据集自动化映射提出了难题。针对这一问题，文章提出了基于深度学习框架—动态卷积神经网络构造的智能录波器配置数据的自动化映射方法；利用文本表征模型word 2vec对数据集描述文本的稀疏文本向量进行词组语义及关联关系的表征；构造动态卷积神经网络并输入文本向量，基于其多层次抽象化学习典型样本特征的特点进行语义规律挖掘与文本分类映射，据此结果实现接口地址配置数据的自动化映射。实际算例表明，基于动态卷积神经网络模型的文本分类方法语义分析能力强，分类精度高，有效提升了智能录波器配置数据自动化映射的准确率。

英文摘要:

The substation configuration description (SCD) file of an intelligent substation contains a large number of intelligent electronic devices (IEDs) configuration datasets of the intelligent secondary device data output interface address. Mapping these datasets to the intelligent recorder is a basic step for the recorder to accurately collect the operation information of IEDs. The current mainstream mapping method is to map the configuration data manually according to the corresponding output interface description text. When the number of IEDs is extremely large, the configuration workload is large either, and since the text descriptions have a certain degree of irregularity, it also poses a problem for the automatic mapping of the datasets. Aiming at this problem, this paper proposes an automatic mapping method of IEDs configuration datasets based on a deep learning framework-dynamic convolutional neural network (DCNN) model. Firstly, the text representation model word 2vec is utilized to represent the sparse text vectors of configuration datasets text descriptions as well as their phrase semantics and association relationships. Then, text vectors will be imported in DCNN, based on its multilayer abstract learning characteristics of typical sample features, which can perform semantic law mining and automatic mapping. The configuration datasets of intelligent recorder will be mapped automatically based on the text mapping result. The practical example shows that the text classification method based on DCNN model has strong semantic analysis ability and high classification accuracy, which effectively improves the accuracy of automatic mapping of intelligent recorder configuration data.

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