Evaluating the computation times of real-time algorithms for power system modeling and state prediction

J. Felder, A. Chakrabortty
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Abstract

This paper presents a comparative study of three real-time algorithms for power system model identification, parameter estimation and state prediction using real-time Phasor Measurement (PMU) data available from various selected nodes in a power system. Current modeling and state estimation algorithms in power control centers only use limited amount of data, leading to local observability. Our approach, on the other hand, is to use data from wide regions in the grid to gain insight on the global health of the system. The two main challenges for our approach are, therefore, the large size of the system and the large amount of measured data. Three specific algorithms, namely the Eigenvalue Realization Algorithm, linear least squares and state observer method, are used for this purpose. The first algorithm identifies the global system dynamics from PMU data in real-time, the second relaxes the identification problem as a parameter estimation problem, while the third generates estimate of the global state and, thereafter, computes the impulse response of a selected oscillation mode depending on the participation of that mode on the chosen output. The performance of these three methods is then compared in terms of their computational time delays and accuracy of prediction.
评估电力系统建模和状态预测实时算法的计算次数
本文对电力系统模型识别、参数估计和状态预测的三种实时算法进行了比较研究,这些算法利用了电力系统中各个选定节点的实时相量测量(PMU)数据。目前电力控制中心的建模和状态估计算法仅使用有限的数据量,导致局部可观测性。另一方面,我们的方法是使用网格中广泛区域的数据来深入了解系统的全球健康状况。因此,我们的方法面临的两个主要挑战是系统的大尺寸和大量的测量数据。为此使用了三种具体算法,即特征值实现算法、线性最小二乘法和状态观测器方法。第一种算法实时地从PMU数据中识别全局系统动态,第二种算法将识别问题简化为参数估计问题,而第三种算法生成全局状态估计,然后根据该模式对所选输出的参与计算所选振荡模式的脉冲响应。然后比较了这三种方法的计算时延和预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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