| 引用本文: | 戴志辉,贺欲飞,张洪嘉.基于Sentence-MacBERT模型的虚端子自动连接方法[J].电力系统保护与控制,2026,54(02):82-90.[点击复制] |
| DAI Zhihui,HE Yufei,ZHANG Hongjia.An automatic virtual terminal connection method based on the Sentence-MacBERT model[J].Power System Protection and Control,2026,54(02):82-90[点击复制] |
|
| 摘要: |
| 鉴于智能变电站待连接的虚端子数量庞大，传统的连接方法常存在识别效率低、校核工作量大、连接结果不准确等问题。提出一种基于掩码纠错型双向编码器句子嵌入模型(sentence-masked language model as correction bidirectional encoder representations from transformer, Sentence-MacBERT)的虚端子自动连接方法。首先，提取实现虚端子自动连接所需关键信息并进行预处理。其次，构建Sentence-MacBERT虚端子自动连接模型并进行训练，得到最优模型。最后，将预处理后的短地址和中文描述分别输入到该模型中，得到综合句向量并进行余弦相似度匹配，完成智能变电站虚端子自动连接。结果表明，相比于传统的虚端子自动连接方法，该方法的连接效率更高，且准确率达到94.38%，实现了虚端子的准确连接。 |
| 关键词: 虚端子 智能变电站 Sentence-MacBERT 深度学习 文本匹配 |
| DOI：10.19783/j.cnki.pspc.250335 |
| 投稿时间：2025-03-31修订日期：2025-07-01 |
| 基金项目:国家自然科学基金项目资助(51877084) |
|
| An automatic virtual terminal connection method based on the Sentence-MacBERT model |
| DAI Zhihui,HE Yufei,ZHANG Hongjia |
| (Hebei Key Laboratory of Distributed Energy Storage and Microgrid
(North China Electric Power University), Baoding 071003, China) |
| Abstract: |
| In view of the large number of virtual terminals to be connected in smart substations, traditional connection methods often suffer from low identification efficiency, large verification workload, and inaccurate connection results. To address these issues, an automatic virtual terminal connection method based on the sentence-masked language model as correction bidirectional encoder representations from transformer (Sentence-MacBERT) model is proposed. First, the key information required for the automatic virtual terminal connection is extracted and preprocessed. Second, the Sentence-MacBERT virtual terminal automatic connection model is constructed and trained to obtain the optimal model. Finally, the preprocessed short addresses and Chinese descriptions are separately input into the model to obtain the comprehensive sentence embeddings, and cosine similarity matching is carried out to complete the automatic connection of virtual terminals in smart substations. The results show that, compared with traditional virtual terminal automatic connection methods, the proposed method achieves higher connection efficiency, with an accuracy of 94.38%, enabling accurate virtual terminal connection. |
| Key words: virtual terminal smart substation Sentence-MacBERT deep learning text matching |
