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A Physics Informed Machine Learning Method for Power System Model Parameter Optimization (2309.16579v1)
Published 28 Sep 2023 in eess.SY and cs.SY
Abstract: This paper proposes a gradient descent based optimization method that relies on automatic differentiation for the computation of gradients. The method uses tools and techniques originally developed in the field of artificial neural networks and applies them to power system simulations. It can be used as a one-shot physics informed machine learning approach for the identification of uncertain power system simulation parameters. Additionally, it can optimize parameters with respect to a desired system behavior. The paper focuses on presenting the theoretical background and showing exemplary use-cases for both parameter identification and optimization using a single machine infinite busbar system. The results imply a generic applicability for a wide range of problems.
- VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., “Der digitale zwilling in der netz- und elektrizitätswirtschaft,” VDE Studie, 2023.
- F. Mahr and J. Jaeger, “Advanced fault ride through operation of grid-forming voltage source converters using a model predictive controller,” in 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), 2021, pp. 2248–2255.
- G. Kordowich, M. Jaworski, T. Lorz, C. Scheibe, and J. Jaeger, “A hybrid protection scheme based on deep reinforcement learning,” in 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). IEEE, 2022, pp. 1–6.
- Z. Zhang, D. Zhang, and R. C. Qiu, “Deep reinforcement learning for power system: An overview,” CSEE Journal of Power and Energy Systems, 2019.
- M. Jerosolimski and L. Levacher, “A new method for fast calculation of jacobian matrices: automatic differentiation for power system simulation,” IEEE Transactions on Power Systems, vol. 9, 1994.
- S. Ratra, P. Singh, and R. Tiwari, “A novel approach for modelling of tcsc in load flow solution using n-r and automatic differentiation,” in 2015 Annual IEEE India Conference (INDICON). IEEE, 2015.
- Z. Wei, X. Yang, G. Sun, Y. Sun, and P. Ju, “Wind power system state estimation with automatic differentiation technique,” International Journal of Electrical Power & Energy Systems, vol. 53, pp. 297–306, 2013.
- W. Gao, E. V. Solodovnik, and R. A. Dougal, “Symbolically aided model development for an induction machine in virtual test bed,” IEEE Transactions on Energy Conversion, vol. 19, no. 1, pp. 125–135, 2004.
- G. Geng, V. Ajjarapu, and Q. Jiang, “Application of automatic differentiation in power system trajectory sensitivity analysis,” in 2014 IEEE PES T&D Conference and Exposition. IEEE, 2014, pp. 1–5.
- G. S. Misyris, A. Venzke, and S. Chatzivasileiadis, “Physics-informed neural networks for power systems.”
- A. K. Maier, C. Syben, B. Stimpel, T. Würfl, M. Hoffmann, F. Schebesch, W. Fu, L. Mill, L. Kling, and S. Christiansen, “Learning with known operators reduces maximum training error bounds,” Nature machine intelligence, vol. 1, no. 8, pp. 373–380, 2019.
- B. Huang and J. Wang, “Applications of physics-informed neural networks in power systems - a review,” IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 572–588, 2023.
- J. Stiasny, G. S. Misyris, and S. Chatzivasileiadis, “Physics-informed neural networks for non-linear system identification for power system dynamics,” in 2021 IEEE Madrid PowerTech. IEEE, 2021, pp. 1–6.
- S. Lakshminarayana, S. Sthapit, and C. Maple, “Application of physics-informed machine learning techniques for power grid parameter estimation,” Sustainability, vol. 14, no. 4, p. 2051, 2022.
- A. Hamid, D. Rafiq, S. A. Nahvi, and M. A. Bazaz, “Power grid parameter estimation using sparse identification of nonlinear dynamics,” in 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP). IEEE, 2022, pp. 1–6.
- G. Kordowich and J. Jaeger, “Supplementar material: Parameter identification and optimization of power system models using the backpropagation method,” 2023. [Online]. Available: https://github.com/georgkordowich/power-system-parameter-optimization
- P. Kundur, “Power system stability and control,” vol. 10, 2007.
- H. Haugdal and K. Uhlen, “An open source power system simulator in python for efficient prototyping of wampac applications.” [Online]. Available: https://arxiv.org/pdf/2101.02937
- Jan Machowski, Janusz W.Bialek and James R.Bumby, “Power system dynamics.”
- A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017.
- NEPLAN AG, “Exciter models, power system stabilizer models.” [Online]. Available: https://www.neplan.ch/downloads-2/