Abstract
Mapping the Intelligent Electronic Devices (IEDs) output interface address description datasets to the intelligent recorder is the groundwork for the recorder to accurately collect IEDs' operation information. These datasets, which are also intelligent recorder configuration datasets, are included in the Substation Configuration Description (SCD) file of an intelligent substation. The mainstream mapping method is manually mapping these datasets based on output interface Chinese description texts. When the number of IEDs is extremely large, the manual operation often takes a huge amount of time, together with the higher labor cost. And since the Chinese description texts 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). Firstly, it uses the word representation model Word2vec to vectorize words in Chinese description texts as well as their semantics relationships. Then word vectors will be imported in the 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 automatically mapped based on the Chinese descriptions mapping result. The Practical example shows that the Chinese description texts classification method based on the Dynamic Convolutional Neural Network model has strong semantic analysis ability and high classification accuracy, which effectively improves the accuracy of automatic mapping of intelligent recorder configuration data.