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Abstract This work highlights the use of half-implicit numerical integration in the context of the index three differential algebraic equations (DAEs) of multibody dynamics. Although half-implicit numerical integration is well established for ordinary differential equations problems, to the best of our knowledge, no formal discussion covers its use in the context of index three DAEs of multibody dynamics. We wish to address this since when compared to fully implicit methods, half-implicit integration has two attractive features: (i) the solution method does not require the computation of the Jacobian associated with the constraint, friction, contact, or user-defined applied forces; and (ii) the solution is simpler to implement. Moreover, for nonstiff problems, half-implicit numerical integration yields a faster solution. Herein, we outline the numerical method and demonstrate it in conjunction with three mechanisms. We report on convergence order behavior and solution speed. The Python software developed to generate the results reported is available as open in a public repository for reproducibility studies.
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Machine-Learning-Based Online Transient Analysis via Iterative Computation of Generator Dynamics
Transient analysis is vital to the planning and operation of electric power systems. Traditional transient analysis utilizes numerical methods to solve the differential-algebraic equations (DAEs) to compute the trajectories of quantities in the grid. For this, various numerical integration methods have been developed and used for decades. On the other hand, solving the DAEs for a relatively large system such as power grids is computationally intensive and is particularly challenging to perform online. In this paper, a novel machine learning (ML) based approach is proposed and developed to predict post-contingency trajectories of a generator in the time domain. The training data are generated by using an off-line simulation platform considering random disturbance occurrences and clearing times. As a proof-of-concept study, the proposed ML-based approach is applied to a single generator. A Long Short Term Memory (LSTM) network representation of the selected generator is successfully trained to capture the dependencies of its dynamics across a sufficiently long time span. In the online assessment stage, the LSTM network predicts the entire post-contingency transient trajectories given initial conditions of the power system triggered by system changes due to fault scenarios. Numerical experiments in the New York/New England 16-machine 86-bus power system show that the trained LSTM network accurately predicts the generator’s transient trajectories. Compared to existing numerical integration methods, the post-disturbance trajectories of generator’s dynamic states are computed much faster using the trained predictor, offering great promises for significantly accelerating both offline and online transient studies.
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- Award ID(s):
- 2025152
- PAR ID:
- 10297996
- Date Published:
- Journal Name:
- IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SmartGridComm47815.2020.9302975
