Data-driven Forced Oscillation Localization using Inferred Impulse Responses
arXiv (Cornell University)(2023)
摘要
Poorly damped oscillations pose threats to the stability and reliability of
interconnected power systems. In this work, we propose a comprehensive
data-driven framework for inferring the sources of forced oscillation (FO)
using solely synchrophasor measurements. During normal grid operations,
fast-rate ambient data are collected to recover the impulse responses in the
small-signal regime, without requiring the system model. When FO events occur,
the source is estimated based on the frequency domain analysis by fitting the
least-squares (LS) error for the FO data using the impulse responses recovered
previously. Although the proposed framework is purely data-driven, the result
has been established theoretically via model-based analysis of linearized
dynamics under a few realistic assumptions. Numerical validations demonstrate
its applicability to realistic power systems including nonlinear, higher-order
dynamics with control effects using the IEEE 68-bus system, and the 240-bus
system from the IEEE-NASPI FO source location contest. The generalizability of
the proposed methodology has been validated using different types of
measurements and partial sensor coverage conditions.
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关键词
forced oscillation localization,inferred impulse
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