Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation

Subhash Lakshminarayana, Saurav Sthapit, Carsten R. Maple

Sustainability 2022 article cited by 28

Source Open-access PDF More in Engineering & Materials

importance 6/10 ai assisted Power System Optimization and Stability power grid operations stage: research

Problem

Estimating unknown power grid parameters such as inertia and damping coefficients from observed system dynamics.

Why it matters

Accurate parameter estimates are needed for stable monitoring and operation of power grids.

What the AI did

Proposed a SINDy-based sparse regression algorithm for parameter estimation and benchmarked it against physics-informed neural networks (PINN) in simulations on IEEE bus systems.

Results

SINDy outperformed PINN over a wide range of system parameters and grid architectures; no numeric metrics stated

Impact

Simulation evidence that physics-informed sparse regression can estimate grid parameters better than PINNs.

Record

Venue	Sustainability
Published	2022
Work type	article
Authors	Subhash Lakshminarayana, Saurav Sthapit, Carsten R. Maple
Citations	28 (OpenAlex, at import time)
References	29
Model or method	SINDy; physics-informed neural networks (PINN)
Organization	not stated in source
Primary source	https://doi.org/10.3390/su14042051
Open access	gold
Keywords	physics-informed ML, SINDy, PINN, power grid, parameter estimation

Importance rating

6 / 10

significant — thousands to millions affected

Power grid parameter estimation supports stable monitoring and operation of critical grid infrastructure; abstract shows only IEEE bus simulations, no real-world scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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