Dynamic Parameter Estimation with Physics-based Neural Ordinary Differential Equations
- University of California, Riverside,Department of Electrical and Computer Engineering,Riverside,California,USA,92507; University of California, Riverside
- University of California, Riverside,Department of Electrical and Computer Engineering,Riverside,California,USA,92507
Accurate estimation of dynamic parameters of gen-erators is crucial to building a reliable model for dynamical studies and reliable operation of the power system. This paper develops a physics-based neural ordinary differential equations (ODE) approach to learn the parameters of generator dynamic model using phasor measurement units (PMU) data. We design a physics-based neural network to represent the swing equations of the power system dynamics. A loss function is defined as the difference between dynamic simulation results from the physics-based neural networks and pseudo PMU measurements. The parameters of generator dynamic model are iteratively updated using the neural ODEs and the adjoint method. By exploiting the mini-batch scheme in neural ODE training, the parameter estimation performance is significantly improved. Numerical study results on a 3-machine 9-bus system show that the proposed algorithm outperforms state-of-the-art baseline method in both computation time and dynamic parameter estimation accuracy.
- Research Organization:
- University of California, Riverside
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- OE0000916
- OSTI ID:
- 1958412
- Report Number(s):
- DOE-UCR-IEEE-PES-GM-2022
- Journal Information:
- 2022 IEEE Power & Energy Society General Meeting (PESGM), Journal Name: 2022 IEEE Power & Energy Society General Meeting (PESGM)
- Country of Publication:
- United States
- Language:
- English
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