Short text data model of secondary equipment faults in power grids based on LDA topic model and convolutional neural network

Abstract

With the comprehensive development of the smart grid, along with the large amount of operational data generated by the operation of the power grid, a lot of attention has been paid to the short-text information about the secondary electrical equipment failures that have occurred. This article analyzes the fault data that occurs during the operation of the secondary equipment. With reference to the general process of Chinese natural language processing, considering the overall characteristics of the fault information, the LDA topic model is used to generate a topic text model of short text data. For local features, use the Word2Vec word vector model is used for characterization. Finally, convolutional neural network (CNN) is used to categorize the fault categories of the information, and a short text classification model of secondary equipment in smart grid based on the LDA topic model and CNN is proposed. The example results show that the proposed Chinese short text classification model can improve the classification accuracy, and the classification effect is also considerable.