| 引用本文: | 邹世豪,曹永吉,刘志文,等.计及多时间尺度电压失稳模式的电网薄弱环节辨识[J].电力系统保护与控制,2024,52(21):35-49.[点击复制] |
| ZOU Shihao,CAO Yongji,LIU Zhiwen,et al.Identification of grid weak link considering voltage instability patterns at multiple time scales[J].Power System Protection and Control,2024,52(21):35-49[点击复制] |
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| 摘要: |
| 针对电网静态和暂态电压失稳特性差异化明显、安全稳定分析难以兼顾的问题，提出一种计及多时间尺度电压失稳模式的电网薄弱环节辨识方法。建立面向静态和暂态电压稳定评估的输入特征，采用主成分分析方法降低静态电压特征的维度，并利用改进k-means++方法对电网进行分区，筛选各分区的表征节点作为暂态电网稳定的待评估节点。量化表征电网静态和暂态电压失稳的风险等级，构建基于深度置信网络(deep belief networks, DBN)的多时间尺度电压稳定一体化评估模型，以提取失稳模式。分析不同电压失稳模式与电网线路间的耦合关系，建立量化指标以辨识电网薄弱环节。最后，通过算例分析验证了所提方法的有效性。 |
| 关键词: 静态电压稳定 暂态电压稳定 风险等级量化 深度学习 薄弱环节辨识 |
| DOI：10.19783/j.cnki.pspc.240055 |
| 投稿时间：2024-01-11修订日期：2024-04-10 |
| 基金项目:国家自然科学基金项目资助(52177096)；山东省自然科学基金项目资助(ZR2021QE133) |
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| Identification of grid weak link considering voltage instability patterns at multiple time scales |
| ZOU Shihao1,CAO Yongji2,LIU Zhiwen1,LI Changgang1 |
| (1. School of Electrical Engineering, Shandong University, Jinan 250061, China;
2. Academy of Intelligent Innovation, Shandong University, Jinan 250101, China) |
| Abstract: |
| The significant differences in the instability mechanism and process of static and transient voltage makes united analysis a challenging problem. An approach is proposed to identify weak grid links considering the voltage instability patterns at multiple time scales. The input features for static and transient voltage stability assessment are established. Principal component analysis is used for dimensionality reduction, and the improved k-means++ method is used to partition the power grid and select representative buses for transient voltage analysis. The risk levels of power system static and transient voltage instability are quantified, and a united multi-time-scale voltage stability assessment model is constructed based on deep belief networks (DBN) to extract voltage instability patterns. The coupling between voltage instability patterns and power grid links is analyzed. Quantitative indices are built to identify the power grid weak links. Finally, the effectiveness of the proposed method is validated through case studies. |
| Key words: static voltage stability transient voltage stability risk level quantification deep learning weak link identification |
