基于光滑变结构滤波器的电力系统动态估计

Ibrahim Al-Omari, A. Rahimnejad, S. Gadsden, M. Moussa, H. Karimipour
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引用次数: 5

摘要

随着分布式能源的集成,传统的电力系统向现代化的智能电网发展。尽管智能电网为更可靠和安全的能源管理开辟了可能性,但它们对电网的实时监测和控制提出了新的挑战。状态估计是保证系统可靠控制的关键功能之一。本文将光滑变结构滤波器(SVSF)应用于电力系统动态状态估计。SVSF是一种基于预测校正的方法,可以应用于线性和非线性系统,具有处理系统不确定性的能力。在具有无限母线网络的单机上的仿真结果表明,与扩展卡尔曼滤波(EKF)和无气味卡尔曼滤波(UKF)相比,所提出的支持向量滤波具有优越性。结果表明,与EKF和UKF方法相比,该方法在性能上具有显著的平滑性和准确性;特别是在测量异常值存在的情况下。
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 Dynamic State Estimation Using Smooth Variable Structure Filter
With the integration of distributed energy resources (DER) traditional power systems evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on real-time monitoring and control of the power grid. State estimation is a key function which plays a vital role in reliable system control. In this paper, the smooth variable structure filter (SVSF) is used for power system dynamic state estimation (DSE). SVSF is a predictor-corrector based approach which can be applied to both linear and nonlinear system with the ability to deal with the system uncertainties. The simulation results on a single machine with infinite bus power network shows the superiority of the proposed SVSF compared to extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of the proposed method show a significant smoothness and accuracy in its performance compared to those obtained from EKF and UKF approaches; in particular, in the presence of measurement outliers.
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