Adelaide Power Systems Summer School 2020
Physics-Informed Neural Networks for Power Systems
Transforming Neural Networks to MILP
Databases for Data-Driven Methods on Power Systems
Convex Formulation of AC-OPF for Distribution Grids
Download the assignment material here: Assignment Material
[1] | G. S. Misyris, A. Venzke, S. Chatzivasileiadis, Physics-Informed Neural Networks for Power Systems, accepted at IEEE PES General Meeting 2020, Montreal, Canada. [ paper ] |
[2] | J. Stiasny, G. S. Misyris, S. Chatzivasileiadis, Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics, submitted, 2020. [ paper ] |
[1] | A. Venzke, D. T. Viola, J. Mermet-Guyennet, G. S. Misyris, S. Chatzivasileiadis, Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow to Mixed-Integer Linear Programs, submitted, 2020. [ paper ] |
The two 14-bus system databases are considered complete. More information about these can be found here: Instructions about IEEE 14-bus databases
The available databases have been based on the following papers:[1] | F. Thams, A. Venzke, R. Eriksson, S. Chatzivasileiadis, Efficient Database Generation for Data-Driven Security Assessment of Power Systems, IEEE Transactions on Power Systems, vol 35, no. 1, pp. 30-41, Jan. 2020. [ paper ] |
[2] | A. Venzke, D.K. Molzahn, S. Chatzivasileiadis, Efficient Creation of Datasets for Data-Driven Power System Applications, Accepted at the 21st Power System Computation Conference (PSCC), 2020. [ paper ] |