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Application of physics-informed machine learning techniques for power grid parameter estimation
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Lakshminarayana, Subhash, Sthapit, Saurav and Maple, Carsten (2022) Application of physics-informed machine learning techniques for power grid parameter estimation. Sustainability, 14 (4). e2051. doi:10.3390/su14042051
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WRAP-application-physics-informed-machine-learning-techniques-power-grid-parameter-estimation-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (473Kb) | Preview |
Official URL: https://doi.org/10.3390/su14042051
Abstract
Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-informed neural networks (PINN) approach applied to parameter estimation. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN algorithm in estimating the power grid parameters over a wide range of system parameters (including high and low inertia systems) and power grid architectures. Particularly, in case of the slow dynamics system, the proposed SINDy algorithms outperforms the PINN algorithm, which struggles to accurately determine the parameters. Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation. Furthermore, we present an extension of the SINDy algorithm to a scenario where the operator does not have the exact knowledge of the underlying system model. We also present a decentralised implementation of the SINDy algorithm which only requires limited information exchange between the neighbouring nodes of a power grid.
| Item Type: | Journal Article | ||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) |
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| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Parameter estimation, Electric power systems -- Computer simulation, Electric power systems -- Mathematical models, Machine learning | ||||||
| Journal or Publication Title: | Sustainability | ||||||
| Publisher: | MDPI | ||||||
| ISSN: | 2071-1050 | ||||||
| Official Date: | 11 February 2022 | ||||||
| Dates: |
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| Volume: | 14 | ||||||
| Number: | 4 | ||||||
| Article Number: | e2051 | ||||||
| DOI: | 10.3390/su14042051 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
| RIOXX Funder/Project Grant: |
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