Machine Learning Assisted Inertia Estimation Using Ambient Measurements

Tuo, Mingjian; Li, Xingpeng

doi:10.1109/tia.2023.3269732

Cited by 8 publications

(1 citation statement)

References 27 publications

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“…This method is used in [6], [7], [8], [9], for example, for the estimation of system inertia, and the difference between them lies in the specific identification method used and the way of data pre-processing and post-processing. In addition, with the advancement of Artificial Intelligence technology, there are also approaches that use machine learning for estimation [10], [11]. For the latter, the overall morphological characteristics of the system during oscillation are often analytically processed to estimate system inertia based on the generator swing equation, using different fitting and equivalence methods.…”

Section: Introductionmentioning

confidence: 99%

A Multifunctional Online Estimation Method for Synchronous Inertia of Power Systems Using Short-Time Phasor Transient Measurement Data With Linear Least Square Method After Disturbance

Wang,

Yokoyama,

Baba

2024

IEEE Access

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This paper proposes a novel method for estimating the inertia, damping, and mechanical power variation of power systems using short-term phasor measurement data during transient events. The method applies a linear least square optimization technique to the generator swing equation and utilizes a pair of sliding windows to capture the dynamic changes of the system. The method can estimate the inertia under different types of disturbances in real-time and track the inertia changes caused by secondary disturbances. The method is validated using simulations in the IEEE New England 10-machine system and compared with existing methods.

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