Topology adaptivity #8
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Damowerko
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May 21, 2025
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Damowerko
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Overall good implementation of OPFDataset. One branch is not handled. Commented out code and debug logic should be removed. Some other comments.
src/opf/dataset.py
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| data["gen"].Sg = self.Sg[index] | ||
| if self.dual_graph: | ||
| data["branch"].Sf = self.Sf[index] | ||
| data["branch"].St = self.St[index] |
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This case isn't handled.
src/opf/dataset.py
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| data["bus", "branch", "bus"].Sf = self.Sf[index] | ||
| data["bus", "branch", "bus"].St = self.St[index] | ||
| # remove any branches with br_status=0 | ||
| br_mask = self.br_status[index].bool() |
| def case_path(self): | ||
| return Path(self.data_dir / f"{self.case_name}.json") | ||
| # TODO: eventually move this to front end | ||
| if self.case_name == "case118_ieee": |
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Is this logic necessary? Can't I simply provide case_name case118_ieee_removed_branch?
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| Sd = torch.zeros((n_samples, n_bus, 2)) | ||
| Sd[:, self.powerflow_parameters.load_bus_ids, :] = load | ||
| # branch_status = self.powerflow_parameters ...?? |
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don't commit commented out code
src/opf/dataset.py
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| ) | ||
| # I am indexing from the end, so that I can change the size of the training dataset | ||
| # without changing wich samples are used for testing and validation | ||
| # # I am indexing from the end, so that I can change the size of the training dataset |
| # no... this is in NOT solved... missing some logic here | ||
| continue | ||
| end | ||
| println("branch $br_removed_index found to be feasibile") |
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Remove the debug logging.
| network_data["branch"][branch_to_remove]["br_status"] = 0 | ||
| branch_to_remove = rand(keys(_network_data["branch"])) | ||
| _network_data["branch"][branch_to_remove]["br_status"] = 0 | ||
| # br_removed_index = parse(Int, branch_to_remove) |
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Remove the comented out code.
| "solve_time" => Array{Float64}(undef, n_samples), | ||
| "objective" => Array{Float64}(undef, n_samples), | ||
| ) | ||
| br_removed_index = "" |
| result, solved = label_network(network_data, load) | ||
| result, solved = label_network(_network_data, load) | ||
| if !solved | ||
| # no... this is in NOT solved... missing some logic here |
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| check_assumptions!(network_data) | ||
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| # data = generate_samples_numpy(network_data, n_samples, min_load, max_load, remove_random_branch) |
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