Data-driven Localization and Estimation of Disturbance in the Interconnected Power System
Machine Learning
2018-06-06 v1 Machine Learning
Abstract
Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data.
Keywords
Cite
@article{arxiv.1806.01318,
title = {Data-driven Localization and Estimation of Disturbance in the Interconnected Power System},
author = {Hyang-Won Lee and Jianan Zhang and Eytan Modiano},
journal= {arXiv preprint arXiv:1806.01318},
year = {2018}
}