2018/06/04 by Hyang-Won Lee, Jianan Zhang, Lee, Hyang-Won +3 · 1 citation
Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Power System Optimization and Stability #Structural Health Monitoring Techniques
paper · pdf · doi:10.48550/arxiv.1806.01318
openalex publication_date 2018/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying the location of a disturbance and its magnitude is an important\ncomponent for stable operation of power systems. We study the problem of\nlocalizing and estimating a disturbance in the interconnected power system. We\ntake a model-free approach to this problem by using frequency data from\ngenerators. Specifically, we develop a logistic regression based method for\nlocalization and a linear regression based method for estimation of the\nmagnitude of disturbance. Our model-free approach does not require the\nknowledge of system parameters such as inertia constants and topology, and is\nshown to achieve highly accurate localization and estimation performance even\nin the presence of measurement noise and missing data.\n