| 引用本文: | 王洪彬,周念成,黄睿灵,等.基于深度学习的110 kV电网监控信号语义解析及态势感知模型[J].电力系统保护与控制,2023,51(2):160-168.[点击复制] |
| WANG Hongbin,ZHOU Niancheng,HUANG Ruiling,et al.110 kV signal semantic analysis and situation awareness model based on deep learningtheory for a power system monitoring system[J].Power System Protection and Control,2023,51(2):160-168[点击复制] |
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| 摘要: |
| 新型电力系统的大力建设对电网监控信号的高效准确识别技术提出了更高的要求。首先分析了Soft-Masked BERT语言模型的基本原理，建立了基于Soft-Masked BERT的信号文本纠错模型。根据国家电网典型事件表梳理了包含常规与故障情况下的“信号语义—电网事件”规则字典。综合上述模型建立了基于RNN的电网态势感知模型，提出了基于深度学习的电网监控信号语义解析及态势感知求解流程。最后，以某地110 kV变电站实际监控信号为测试数据，利用所提RNN模型并结合Pycorrector工具包及Pytorch软件对该地区电网监控信号进行语义解析及态势感知仿真分析，验证了模型的有效性及正确性。 |
| 关键词: 深度学习 电网监控信号语义解析 态势感知 RNN模型 |
| DOI：10.19783/j.cnki.pspc.220743 |
| 投稿时间：2022-05-17修订日期：2022-08-12 |
| 基金项目:国家自然科学基金项目资助(52077017) |
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| 110 kV signal semantic analysis and situation awareness model based on deep learningtheory for a power system monitoring system |
| WANG Hongbin,ZHOU Niancheng,HUANG Ruiling,FAN Bingxin,WANG Qianggang |
| (1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing
University), Chongqing 400044, China; 2. State Grid Chongqing Electric Power Company
Research Institute, Chongqing 401123, China) |
| Abstract: |
| The vigorous construction of new power systems entails higher requirements for the efficient and accurate identification technology for power grid monitoring signals. This paper first analyzes the basic principles of the Soft-Masked BERT language model, and establishes a signal text error correction model based on Soft-Masked BERT. According to the typical information table of the State Grid, the rule dictionary of "signal semantics-grid events" in normal and fault conditions is analysed. Based on the above models, a power grid situation awareness model based on RNN is established, and a semantic analysis of power grid monitoring signals and a situation awareness solution process based on deep learning are proposed. Finally, taking the actual monitoring signal of a 110 kV substation as the test data, the proposed RNN model is used to analyze the semantic analysis and situation awareness simulation analysis of the monitoring signal of the power grid in this area by combining the Pycorector toolkit and the Pytorch software. The validity and correctness of the model are verified.
This work is supported by the National Natural Science Foundation of China (No. 52077017). |
| Key words: deep learning semantic analysis of power grid monitoring signals situation awareness RNN model |
