在韩国电力系统中基于稳定状态数据的动态稳定性评估.
Sungyoon Song1, Sang-Won Min2, Seungmin Jung3
1Tech University of Korea, 237, Sangidaehak-ro, Siheung-si, Gyeonggi-do, South Korea.
Scientific reports
|March 5, 2025
概括
本研究介绍了使用易于获得的稳定状态电网数据来预测转子角度稳定性的模型,克服了高分辨率测量的挑战. 新的框架增强了动态安全评估,用于实际的电力系统运行.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 计算智能是一种计算智能.
背景情况:
- 动态安全评估 (DSA) 和电力系统的稳定性预测传统上依赖于高分辨率的故障后数据,这些数据很难且成本高昂.
- 广泛部署相位测量单元 (PMU) 的不切实际性限制了在现实场景中使用高分辨率数据的可能性.
- 现有的方法通常将稳定性预测视为黑子,缺乏物理解释性.
研究的目的:
- 开发一个转子角度稳定性预测模型,利用易于获得的稳定状态电网数据.
- 解决动态安全评估中高分辨率数据采集的实际局限性.
- 提高电力系统稳定性预测的解释性和效率.
主要方法:
- 一个新的框架,将扩展的等面积标准的物理见解与机器学习技术相结合.
- 特性数据提取策略,以减少输入维度的支持向量机 (SVM) 模型.
- 将时间序列的功率流量数据按月划分,以考虑系统拓变化,并使用5分钟间隔数据进行培训.
主要成果:
- 拟议的框架有效地预测了使用稳定状态前应急数据的转子角度稳定性.
- 在对关键线路故障事件的实时响应中表现出有效性.
- 该方法成功识别了不稳定的病例,并通过提取的特征训练了SVM.
结论:
- 稳定状态数据可以有效地用于转子角度稳定性的预测,为高分辨率数据提供了切实可行的替代方案.
- 开发的框架为动态安全评估提供了更易于解释和更有效的计算方法.
- 这项研究提供了一种可行的解决方案,用于实时增强电力系统的可靠性和安全性.
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