一个强大的ALOHA和基于顺序聚类的模式估计器,用于电源系统中使用同步相位器的低频振荡
Manoranjan Sahoo1, Shekha Rai1
1Department of Electrical Engineering, National Institute of Technology Rourkela, Odisha, India.
ISA transactions
|December 13, 2024
概括
本研究引入了一种可靠的方法,用于估计电力系统中低频模式,这对于小信号稳定性至关重要. 新技术有效处理噪声和异常值,改善电网分析的实时自动化.
科学领域:
- 电力系统工程 电力系统工程
- 信号处理 信号处理
- 控制理论 控制理论
背景情况:
- 准确估计低频模式对于电力系统的小信号稳定性至关重要.
- 现有的方法,如通过旋转不变技术 (TLS-ESPRIT) 来估计信号参数的总最小平方,需要先了解模式号码.
- 当前的模型顺序估计技术对自相关矩阵中的噪声和异常值敏感,阻碍了实时自动化.
研究的目的:
- 提出一种可靠的模式估计技术,用于精确检测电力系统中的低频模式,即使有高差异噪声和异常值.
- 为了克服现有的模型订单估计方法的局限性.
- 提高电力系统稳定性分析的自动化和可靠性.
主要方法:
- 消除基于过器的低等级汉克尔矩阵 (ALOHA) 技术,以创建一个等级缺陷的汉克尔矩阵,减轻相位测量单元 (PMU) 信号中的噪声和异常值.
- 顺序的K-Mean++集群将自相对应矩阵的固有值分为信号和噪声子空间,识别突出的低频模式.
- 使用TLS-ESPRIT估计模型顺序进行模式估计.
主要成果:
- 拟议的ALOHA技术有效地消除了PMU信号中的噪声和异常值.
- 顺序K-Mean++通过区分信号和噪声子空间,准确地检测出主导的低频模式的数量.
- 综合方法提供了可靠的低频模式估计,通过模拟和现实数据验证.
结论:
- 开发的技术为电力系统中低频模式估计提供了强大的解决方案,在噪音和异常条件下优于现有方法.
- 这种方法提高了小型信号稳定性评估的准确性和自动化.
- 在各种合成和现实世界电力系统场景中证实了该方法的有效性.
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