Subset selection for improved parameter estimation in on-line identification of a synchronous generator
Subset selection for improved parameter estimation in on-line identification of a synchronous generator
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DOI：
10.1109/59.744536
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发表时间：
1999-02-01
影响因子：
6.6
通讯作者：
Vélez-Reyes, M
中科院分区：
文献类型：
--
作者：
Burth, M;Verghese, GC;Vélez-Reyes, M
本文探讨了非线性最小二乘参数估计的子集选择，并适用于以前在电力系统文献中研究的测试系统的方法，涉及在线识别的同步发电机模型与许多参数。子集选择将参数划分为良态和病态子集。我们的测试系统，固定的病态参数先验估计（即使这些先验估计是错误的），估计只有剩余的参数，显着提高了估计算法的性能，大大提高了估计参数的质量。它表明，试图估计所有的模型参数，在原来的工作与此测试系统，可以产生非常不可靠的结果。
This paper examines subset selection for nonlinear least squares parameter estimation, and applies the methodology to a test system previously studied in the power system literature, involving the on-line identification of a synchronous generator model with many parameters. Subset selection partitions the parameters into well-conditioned and ill-conditioned subsets. We show for the test system that fixing the ill-conditioned parameters to prior estimates (even if these prior estimates are substantially in error), and estimating only the remaining parameters, significantly improves the performance of the estimation algorithm and greatly enhances the quality of the estimated parameters. It is shown that attempts to estimate all of the model parameters, as done in the original work with this test system, can yield extremely unreliable results.