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.

中文翻译：

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非线性系统中的无模流同步相量数据恢复

本文开发了一种无模型方法来恢复在非线性动力系统中获得的流式同步相量测量中的缺失点。它可以在一段时间内连续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学系统，并利用提升维度中的低秩 Hankel 来表征系统动力学。内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，我们的方法放弃了线性系统的假设，适用于一般非线性系统。该算法计算复杂度低，可以实时实现。