General neural ODE and DAE modules for power system dynamic modeling.

The PyTorch-based ODE solver is developed based on torchdiffeq.

Samples are generated using Py_PSOPS.

-Windows 7, 8, 10

-Linux

-Python 3.6 or later

1. Clone / Pull the codes.

2. Try building neural dynamic models for power system dynamic components such as generator unit, loads, stations, distribution networks, regulators, etc.

Regular neural ODE module with external inputs.

Autoencoder-based neural ODE module with external inputs.

Regular neural DAE module.

Autoencoder-based neural DAE module.

Logger class.

A sample data file can be downloaded at Baidu web storage:

URL: https://pan.baidu.com/s/1P24h0AQqNS9SAwG1K4wW6g

Code：tiiy

1. Currently, the C++-witten PSOPS can support PyTorch C++ API. However, the python API Py_PSOPS of PSOPS can only successfully load PSOPS.dll/PSOPS.so without PyTorch C++ API. We need to find a way to deal with the violation between Python and PyTorch C++ API when using PyTorch C++ API-integrated PSOPS.dll/PSOPS.so.

2. Add comment.

3. Develop more general modules.

4. Improve performance of the general module and the trained models.

[1] T. Xiao, Y. Chen*, T. He, and H. Guan, “Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling,” IEEE Transactions on Power Systems, vol. 38, no. 3, pp. 2666–2678, May 2023, doi: 10.1109/TPWRS.2022.3194570, arxiv.

[2] T. Xiao, Y. Chen*, J. Wang, S. Huang, W. Tong, and T. He, “Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators,” Journal of Modern Power Systems and Clean Energy, vol. 11, no. 2, pp. 401–411, Mar. 2023, doi: 10.35833/MPCE.2022.000099, arxiv.