Modeless Streaming Synchrophasor Data Recovery in Nonlinear Systems
Modeless Streaming Synchrophasor Data Recovery in Nonlinear Systems
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DOI：
10.1109/tpwrs.2019.2939559
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发表时间：
2020-03
影响因子：
6.6
通讯作者：
Yingshuai Hao;Meng Wang;J. Chow
中科院分区：
文献类型：
--
作者：
Yingshuai Hao;Meng Wang;J. Chow
本文提出了一种无模型的方法来恢复流同步相量测量中的非线性动态系统的缺失点。它可以在一段时间内连续地准确地恢复所有通道上同时和连续的数据丢失，而根本不需要对非线性动力学进行建模。其思想是将非线性系统提升为无限维线性动力系统，并利用提升维中的低秩Hankel来表征系统动力学。核技术被用来处理隐式提升函数。与现有的无模型同步相量数据恢复方法相比，该方法放弃了线性系统的假设，适用于一般的非线性系统。该算法计算复杂度低，可以真实的实时实现。在同步相量数据集上的数值实验验证了该方法的有效性。
This paper develops a model-free approach to recover the missing points in streaming synchrophasor measurements obtained in nonlinear dynamical systems. It can accurately recover simultaneous and consecutive data losses across all channels for some time consecutively without modeling the nonlinear dynamics at all. The idea is to lift the nonlinear system to an infinite-dimensional linear dynamical system and exploit the low-rank Hankel in the lifted dimension to characterize the system dynamics. The kernel technique is employed to handle the implicit lifting function. Compared with existing model-free synchrophasor data recovery methods, our approach drops the assumption of linear systems and applies to general nonlinear systems. The algorithm has low computational complexity and can be implemented in real time. The method is validated through numerical experiments on recorded synchrophasor datasets.