| 引用本文: | 王长刚,王先伟,曹 宇,等.基于改进图注意力网络的电力系统脆弱性关键环节辨识[J].电力系统保护与控制,2024,52(15):36-45.[点击复制] |
| WANG Changgang,WANG Xianwei,CAO Yu,et al.Critical link identification of power system vulnerability based on modified graph attention network[J].Power System Protection and Control,2024,52(15):36-45[点击复制] |
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| 摘要: |
| 随着电网的扩大与新能源比例的增加，电网的不确定性和随机性因素增加，危及系统安全运行，寻找出电网中的脆弱性关键环节来保障电网运行时的可靠性就显得尤为重要。针对当前传统电网脆弱性关键环节辨别方法识别速度慢、难以满足电网实际运行要求的问题，提出了基于改进图注意力网络算法(improved graph attention network, IGAT)的电网脆弱性关键环节辨识方法。首先，结合复杂网络理论和电网实际运行数据建立评价指标集。其次，利用IGAT挖掘出电网运行时的各项指标与脆弱性关键环节之间的映射关系，建立脆弱性关键环节辨识模型，并且考虑到训练准确性和效率等需求，对原始的图注意力网络进行优化。再次，通过仿真得到原始数据集，对辨识模型进行训练、验证和测试。最后，利用所述模型应用于改进的IEEE 30节点系统和实际电网中，结果表明所提方法具有可行性，且准确性和速度优于传统方法，有一定的工程利用价值。 |
| 关键词: 脆弱性关键环节 复杂网络理论 图注意力神经网络 运行可靠性 |
| DOI：10.19783/j.cnki.pspc.231482 |
| 投稿时间：2023-11-21修订日期：2024-02-17 |
| 基金项目:吉林省自然科学基金项目资助(YDZJ202101 ZYTS149) |
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| Critical link identification of power system vulnerability based on modified graph attention network |
| WANG Changgang1,2,WANG Xianwei2,CAO Yu1,2,LI Yang1,2,LÜ Qi2,ZHANG Yaoxin3 |
| (1. Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of
Education, Northeast Electric Power University, Jilin 132012, China; 2. Northeast Electric Power University,
Jilin 132012, China; 3. State Grid Tianjin Electric Power Company, Tianjin 300010, China) |
| Abstract: |
| With the expansion of the power grid and the increase of the proportion of new energy sources, the uncertainty and random factors of the power grid increase, endangering the safe operation of the system. It is particularly important to find out the critical links of vulnerability in the power grid to ensure the reliability of the power grid operation. Aiming at the problem that the identification speed of the traditional critical link of vulnerability identification methods is slow and difficult to meet the actual operation requirements of the power grid, the improved graph attention network (IGAT) based identification method of the critical link is proposed. First, the evaluation index set is established by combining the complex network theory and the actual operation data of power grid. Secondly, IGAT is used to dig out the mapping relationship between various indicators and critical links of vulnerability during the operation of the power grid, establish the identification model of critical links of vulnerability, and optimize the original graph attention network considering the training accuracy and efficiency. Thirdly, the original data set is obtained through simulation, and the identification model is trained, verified and tested. Finally, the model is applied to the improved IEEE 30-node system and the actual power grid, and the results show that the proposed method is feasible, and the accuracy and speed are better than that of traditional methods. It has certain engineering utilization value. |
| Key words: critical link of vulnerability complex network theory graph attention neural networks operational reliability |
