Data-driven Localization and Estimation of Disturbance in the Interconnected Power System
This addresses stable operation for power system operators, but it is incremental as it applies existing regression methods to a specific domain.
The paper tackles the problem of localizing and estimating disturbances in interconnected power systems using a model-free approach based on frequency data from generators, achieving highly accurate performance even with noise and missing data.
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.