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,6,3,4,5,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
The Substation Configuration Description (SCD) file of an intelligent substation contains a large number of Intelligent Electronic Devices (IEDs) configuration datasets of the output interface address. Mapping these datasets to the intelligent recorder is a basic step for the recorder to accurately collect IEDs’ operation information. The current mainstream mapping method is to map the corresponding configuration data manually according to the output interface description text. When the number of IEDs is extremely big, 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 the automatic mapping technology of IEDs configuration datasets based on a deep learning framework—Dynamic Convolutional Neural Network (DCNN) model. Firstly, it uses the text representation model word2vec 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, which, based on its multilayer abstract learning characteristics of typical sample features, 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.