利用子空间识别进行电力系统监控

A. Mohammadi, H. Khaloozadeh, R. Amjadifard
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引用次数: 0

摘要

本文提出了一种监测和预测电力系统振荡不稳定性的新指标(Hopf分岔)。考虑到现代控制技术,该指标利用了整个电力系统的阻尼信息。因此,我们称其为DMI (Damping Matrix Index)。它很容易使用电力系统可用的信号,如机电扭矩,同步电机的速度和角度来预测振荡不稳定性。由于每个监测指标的值隐藏在其估计方法之后,并且所提出的指标是基于电力系统的状态空间模型,因此我们使用子空间系统识别(SSI)算法来估计所提出的指标。基于SSI技术和提出的指标,提出了一种电力系统监测算法。利用所提出的指标对两区四机电力系统的模拟测量进行了测试和仿真。结果表明,与其他已知的振荡不稳定性指标相比,DMI具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power system monitoring using subspace identification
In this paper, we proposed a novel index for monitoring and prediction of oscillatory instability (Hopf Bifurcation) in power systems. Considering modern control techniques, the index uses damping information of the whole power system. Therefore, we call it as DMI (Damping Matrix Index). It easily uses power system available signals such as electro-mechanical torques, speeds and angles of synchronous machines to predict oscillatory instability. Since the values of each monitoring index hides behind its estimation method and the proposed index is based on state space model of power system, we use Subspace System Identification (SSI) algorithms to estimate the proposed index. Based on SSI techniques and the proposed index, we suggest an algorithm for power system monitoring. Tests and simulations have been conducted using the proposed index on simulated measurements of a two-area 4-machine power system. Results express good performance of DMI in comparison with other well-known oscillatory instability indices.
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