基于BERT-DSA-CNN和知识库的电网调控在线告警识别

电网调控告警识别是实现智能电网调度的重要环节。为提高电网调控告警识别的准确率，针对电网数据量庞大、有效信息提取困难、传统知识库知识迁移能力较差等问题，提出一种基于BERT-DSA-CNN和知识库的电网调控在线告警识别方法。首先在自然语言处理-深度学习的文本数据挖掘架构基础上，经过分词、去停用词等步骤，利用BERT模型获取电网调控告警信息词向量。然后将词向量输入CNN深度学习模型进行训练，并根据电网告警信息的特点引入DSA机制对CNN模型进行改进。最后提出了融合深度学习模型和传统知识库的电网调控在线告警识别方案。通过大量的算例结果分析得出，该方法相比Word2vec、传统CNN、传统知识库、离线学习等方法，具有更高的准确性和有效性，对不同的故障类型均具有较好的识别能力，为工程应用提供了一种思路。

Online alarm recognition of power grid dispatching based on BERT-DSA-CNN and a knowledge base

Power grid control alarm recognition is an important aspect of realizing smart grid dispatching. In order to improve the accuracy of power grid control alarm recognition, in view of the huge amount of grid data, the difficulty of extracting effective information, and the poor ability of traditional knowledge base knowledge migration, a power grid control online alarm recognition method based on BERT-DSA-CNN and knowledge base is proposed. First, using natural language processing-deep learning text data mining architecture, after the steps of word segmentation and removal of stop words, the BERT model is used to obtain the word vector of the power grid control warning information. Then the word vector is input into the CNN deep learning model for training, and the DSA mechanism is introduced according to the characteristics of the power grid warning information. Finally, an online warning recognition scheme for power grid regulation is proposed, one which combines the deep learning model and the traditional knowledge base. Through the analysis of a large number of calculation examples, it is concluded that this method has higher accuracy and effectiveness than Word2 vec, traditional CNN, traditional knowledge base, offline learning and other methods, and has better recognition ability for different types of faults, providing a basis for engineering application.