Online Learning of Power Transmission Dynamics
Online Learning of Power Transmission Dynamics
复制标题
动力传输动力学在线学习
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Chertkov
中科院分区：
文献类型：
--
作者：
A. Lokhov;Marc Vuffray;D. Shemetov;Deepjyoti Deka;M. Chertkov
我们考虑在环境波动的情况下根据带时间戳的 PMU 测量值重建输电电网动态状态矩阵的问题。使用基于最大似然的方法，我们构建了一系列凸估计器，它们根据可用的先验信息来适应问题的结构。所提出的方法是完全数据驱动的，并且不假设任何系统参数的知识。它可以近乎实时地实现，并且需要少量的数据。我们的学习算法可用于模型验证和校准，也可应用于系统稳定性、强迫振荡检测、发电重新调度以及系统状态估计等相关问题。
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.
影响因子：
6.6
作者：
Song Guo;S. Norris;J. Bialek
通讯作者：
Song Guo;S. Norris;J. Bialek