# Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

arXiv: https://arxiv.org/abs/1806.01318 · HTML: https://mlsanity.com/paper/1806.01318/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Suppressing unknown disturbances to dynamical systems using machine learning](https://mlsanity.com/paper/2307.03690/) (2023)
- [Detecting disturbances in network-coupled dynamical systems with machine learning](https://mlsanity.com/paper/2307.12771/) (2023)

## Abstract (arXiv)

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.