Data Generation v4

[sentient-codebot]

Plan for Data Generation v3

1. Use entries in the admittance matrix as edge features.
   1. In this way, we do not need to put extra care on the shunt capacitance of the lines anymore and we do not need to remove them; the generated data are also more realistic.
   2. We should also be able to allow non-zero $G_s$ and $B_s$ because they will also be integrated into the the admittance matrix. (We can allow this only when this does not influence our output format (P, Q, V, theta)
2. The admittance matrix elements are non-zero only where two nodes are directly connected and in the diagonal line. To view them as edge features, we also need to adjust our formulation of the edges. That is, we need to add self-loops at every node to assign the diagonal elements in the admittance matrix.
3. The admittance matrix is symmetric, meaning the according edges are undirected. So we might as well make our edge_index also containing undirected edges, which is different from our v1 and v2 data generation. We perhaps need to adjust our network slightly.
4. Since the meaning of edge features changed, we need to adjust our implementation of the physical loss.

Action Items

• [ ] change the edge feature generation in the script dataset_generator_pandapower_nan.py -> get from nodal admittance matrix AND undirected.
• [ ] make edges undirected and add self-loops in the edge_index variable.
• [ ] remove the "unrealistic" modifications on the C_nf per line.
• [ ] allow non-zero $G_s$ and $B_s$
• [ ] [optional] adjust our sampling method of $P_d$ and $P_g$, as when they are too imbalanced, the power flow would not converge.
• [ ] double check our network inference steps to make sure it align with the new edge formulations.
• [ ] revise the PowerImbalance (physical loss function) to make it align with the new edge features. do sanity checks (with target data). the result should work perfectly, since the admittance matrix should be mathematically sufficient and strict for calculating the power flow.