Power Grid Model IO
Power Grid Model IO is a tool to convert grid data to and from the native data format of power-grid-model. Currently, conversions from Vision excel exports and Pandapower are possible.
Some formats may be similar to the format used by power-grid-model, but use a different underlying data structure. While we do not formally support those formats, you may find examples in this documentation for some of them.
Install from PyPI
You can directly install the package from PyPI.
pip install power-grid-model-io
Install from Conda
If you are using conda, you can directly install the package from conda-forge channel.
conda install -c conda-forge power-grid-model-io
Citations
If you are using Power Grid Model IO in your research work, please consider citing our library using the references in Citation.
Contents
Detailed contents of the documentation are structured as follows.
API Documentation
- power_grid_model_io
- converters
BaseConverterPgmJsonConverterTabularConverterPandaPowerConverterPandaPowerConverter.__init__()PandaPowerConverter.system_frequencyPandaPowerConverter.pp_input_dataPandaPowerConverter.pgm_input_dataPandaPowerConverter.idxPandaPowerConverter.idx_lookupPandaPowerConverter.next_idxPandaPowerConverter.get_individual_switch_states()PandaPowerConverter.get_switch_states()PandaPowerConverter.get_trafo3w_switch_states()PandaPowerConverter.get_trafo_winding_types()PandaPowerConverter.get_trafo3w_winding_types()PandaPowerConverter.get_id()PandaPowerConverter.lookup_id()
IdReferenceFieldsVisionExcelConverter
- data_stores
- data_types
- functions
has_value()value_or_default()value_or_zero()complex_inverse_real_part()complex_inverse_imaginary_part()get_winding()degrees_to_clock()is_greater_than()both_zeros_to_nan()relative_no_load_current()reactive_power()power_wind_speed()get_winding_from()get_winding_to()get_winding_1()get_winding_2()get_winding_3()get_clock()get_clock_12()get_clock_13()reactive_power_to_susceptance()pvs_power_adjustment()
- mappings
- utils
- converters
Contribution