Fault Classification Method for Power Dispatching Log Based on Text Mining

Abstract

The construction of smart grid has inevitably become the direction of the development of the power grid, and the use of data mining technologies to improve the level of data analysis is an important part of the construction of smart grid. In power grid scheduling, as the basis for the dispatcher to record the operation status of the power grid, the scheduling log usually uses short text to record the current state of the power grid, accident handling and abnormal alarm. However, it is lack of further analysis of the log. Aiming at the automatic classification of power grid dispatching log faults, we firstly use the custom power word and word segmentation tool to preprocess the text. After obtaining the TF-IDF feature of the text and creating the Word2Vec feature model, we compare three kinds of text classification algorithms, the nearest neighbor algorithm, Naive Bayes and support vector machine respectively. It is found that the support vector machine method is very effective in classification with a correct rate of 93% in the log short text.