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{"@context":"https://schema.org","@type":"QAPage","mainEntity":{"@type":"Question","name":"State-estimation results of pandapower and power-grid-model are different","text":"
Hello!
\nI have been trying to convert a pandapower-network into power-grid-model and run a state-estimation on both networks. The conversion  with PandaPowerConverter from power-grid-model-io was successful. I added the measurements at the pandapower-buses as voltage and power-sensors to the network modelled in power-grid-model. These networks are attached. There is also a json-file \"idx_lookup.json\" attached which is a serialized version of the idx_lookup-attribute of the PandaPowerConverter and maps the power-grid-model indices to the indices of the elements in the pandapower network tables. Then I executed the following code:
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import pandapower as pp\nfrom power_grid_model import PowerGridModel, ComponentType\nfrom power_grid_model.utils import json_deserialize_from_file\n\npp_net = pp.from_json(\"pp_net.json\")\npgm_input = json_deserialize_from_file(\"pgm_net.json\")\npgm_net = PowerGridModel(pgm_input)\n\npp.estimation.estimate(pp_net)\nstate_estimation_results = pgm_net.calculate_state_estimation(error_tolerance=1e-3)\n
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The results of the both networks are very different. For example the first line (the power-grid-model id 118 matches the id 0 in pandapower):
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In [41]: res_line_est_pgm = pd.DataFrame(state_estimation_results[ComponentType.line])\n\nIn [42]: pp_net.res_line_est.loc[0, \"p_to_mw\"]\nOut[42]: np.float64(-29.727469274206157)\n\nIn [43]: res_line_est_pgm.loc[0, \"p_to\"]*1e-6\nOut[43]: np.float64(-1.040860668477377)\n\nIn [44]: pp_net.res_line_est.loc[0, \"i_from_ka\"]\nOut[44]: np.float64(0.16288191139708483)\n\nDEBUG:asyncio:Using selector: EpollSelector\nIn [45]: res_line_est_pgm.loc[0, \"i_from\"]*1e-3\nOut[45]: np.float64(0.03254555759419108)\n
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Also the result-values for the voltages at the nodes are unrealistic:
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In [59]: res_node_est_pgm = pd.DataFrame(state_estimation_results[ComponentType.node])\n\nIn [60]: res_node_est_pgm.u_pu.describe()\nOut[60]: \ncount    118.000000\nmean       0.177706\nstd        0.026815\nmin        0.115898\n25%        0.147870\n50%        0.183951\n75%        0.203110\nmax        0.220805\nName: u_pu, dtype: float64\n
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For pandapower, they look okay:
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In [61]: pp_net.res_bus_est.vm_pu.describe()\nOut[61]: \ncount    118.000000\nmean       0.927139\nstd        0.060537\nmin        0.793122\n25%        0.882525\n50%        0.930570\n75%        0.962631\nmax        1.079446\n\nName: vm_pu, dtype: float64\n
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Do you have any suggestions on why the results are so different?
\n
idx_lookup.json
\npgm_net.json
\npp_net.json
","upvoteCount":2,"answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"
Hi @Milena1511, thanks for asking the question here. I think they use p.u. voltage at PandaPower, looking at the voltages in your voltage sensors. At Power Grid Model u_measured should be provided in V. I think this is the reason your results are different. Can you check if this is indeed the case?
","upvoteCount":1,"url":"https://github.com/orgs/PowerGridModel/discussions/63#discussioncomment-14152732"}}}