基于WLS和WLAV的状态估计器在实际电力系统中的比较

Yangque Zhu, Yonghui Xie, Ming Wu, Genghui Zhu, Xiao Wang, Lilan Dong
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摘要

状态估计一直是电网能量管理系统的核心。本文介绍了WLS估计器和WLAV估计器。在实际电力系统中比较了两种估计器的收敛性、良好测量率(GMR)和计算效率。结果表明,加权最小绝对值(WLAV)估计器由于存在大量不等式约束,需要更多的迭代和占用更多的CPU时间。值得注意的是,WLAV估计器的鲁棒性使其在面向未知的SE中具有更好的估计精度和更高的GMR。加权最小二乘(加权最小二乘,WLS)估计器本身不能抵抗坏数据的影响,但应用坏数据识别可以有效地提高其GMR和SE的精度。与不等式约束对WLAV估计器计算效率的影响相比,具有不良数据识别的WLS估计器可以保证计算效率并保证SE的GMR。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Comparison of WLS and WLAV Based State Estimator in Practical Power System
State estimation (SE) has been the core of energy management system for power grid. This paper describes the WLS estimator and the WLAV estimator. The convergence, Good Measurement Rate (GMR) and computational efficiency of the two estimators are compared in a practical power system in China. The results show that the Weighted Least Absolute Value (WLAV) estimator requires more iterations and takes up more CPU time due to a large number of inequality constraints. It is worth noting that the robustness of the WLAV estimator makes it has better estimation accuracy and higher GMR in the unknown-oriented SE. Although the Weighted Least Squares (WLS) estimator itself cannot resist the influence of bad data, the application of bad data identification can effectively improve its GMR and accuracy of SE. Compared with the impact of inequality constraints on the computational efficiency of the WLAV estimator, the WLS estimator with bad data identification can guarantee the computational efficiency and ensure the GMR of SE.
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