Vision Conversion

This example illustrates conversion from Vision excel export to power-grid-model input data. They function in a similar way since both are Tabular Converters. We can then calculate power-flow with it or convert to a different formats like PGM JSON.

Vision conversion

Vision example

1. Load the Vision data

To export the Vision file in Excel format, please follow the instructions given in the Vision Manual.

Define source and destination paths:

source_file = "data/vision/example.xlsx"
destination_file = "data/vision/sym_output.json"

Instantiate the converter, optionally with a source file path. Then use load_input_data() to load the data and convert it to power-grid-model data. The additional information that is not used in the powerflow calculation but may be useful to link the results to the source data is stored in extra_info.

%%capture cap --no-stderr
from power_grid_model_io.converters import VisionExcelConverter

converter = VisionExcelConverter(source_file=source_file)
input_data, extra_info = converter.load_input_data()

Let’s investigate the data we have converted, for one of the components: nodes

import pandas as pd

# The node data is stored as a numpy structured array in input_data["node"]
display(input_data["node"])

# We can use pandas to display the data in a convenient tabular format
display(pd.DataFrame(input_data["node"]))

# Notice that the node names were not stored in the numpy array, as we don't need them for the calculations
display({i: extra_info[i]["Name"] for i in input_data["node"]["id"]})

array([(0, 400.), (1, 400.), (2, 400.), (3, 400.)],
      dtype={'names': ['id', 'u_rated'], 'formats': ['<i4', '<f8'], 'offsets': [0, 8], 'itemsize': 16, 'aligned': True})

	id	u_rated
0	0	400.0
1	1	400.0
2	2	400.0
3	3	400.0

{0: 'First Node', 1: 'Fourth Node', 2: 'Second Node', 3: 'Third Node'}

2. Validate the data

Before we run a power flow calculation, it is wise validate the data. The most basic method is to use assert_valid_input_data(), which will raise a ValueError when the data is invalid. For more details on data validation, please consult the validation Example.

from power_grid_model import CalculationType
from power_grid_model.validation import assert_valid_input_data

assert_valid_input_data(input_data, calculation_type=CalculationType.power_flow, symmetric=True)

3. Run the calculation

Run powerflow calculation with the input_data and show the results for nodes.

from power_grid_model import PowerGridModel

pgm = PowerGridModel(input_data=input_data)
output_data = pgm.calculate_power_flow()

display(pd.DataFrame(output_data["node"]))

	id	energized	u_pu	u	u_angle
0	0	1	0.999991	399.996569	-0.000039
1	1	1	0.983232	393.292609	-0.003435
2	2	1	0.987662	395.064831	-0.003235
3	3	1	0.979773	391.909191	-0.006297

Cross referencing objects

The converter has generated unique numerical IDs for all the components in the VisionExcel file, in fact for some special components like Transformer loads, multiple PGM components have been created, each with their own numerical ID. To find out which component belongs to which id, some helper functions have been defined:

print("PGM object #0:", converter.lookup_id(0))
print("PGM object #4:", converter.lookup_id(4))
print("PGM object #7:", converter.lookup_id(7))
print("PGM object #9:", converter.lookup_id(9))

print(
    "Node with Number=1:",
    converter.get_node_id(number=1)
)

print(
    "Cables with Number=6:",
    converter.get_branch_id(table="Cables", number=6)
)

print(
    "Source with Node.Number=1 and Subnumber=1:",
    converter.get_appliance_id(table="Sources", node_number=1, sub_number=1)
)

print(
    "Loads with Node.Number=4 and Subnumber=1:",
    converter.get_appliance_id(table="Loads", node_number=4, sub_number=1)
)

PGM object #0: {'table': 'Nodes', 'key': {'Number': 1}}
PGM object #4: {'table': 'Cables', 'key': {'Number': 6}}
PGM object #7: {'table': 'Sources', 'key': {'Node.Number': 1, 'Subnumber': 1}}
PGM object #9: {'table': 'Loads', 'key': {'Node.Number': 4, 'Subnumber': 1}}
Node with Number=1: 0
Cables with Number=6: 4
Source with Node.Number=1 and Subnumber=1: 7
Loads with Node.Number=4 and Subnumber=1: 9

Saving the data as a JSON file

The data can be stored in a json file using the PgmJsonConverter. The file will be saved in the destination_file path supplied in the constructor.

from power_grid_model_io.converters import PgmJsonConverter

json_converter = PgmJsonConverter(destination_file=destination_file)
json_converter.save(data=output_data, extra_info=extra_info)

For debugging purposes, let’s check the output JSON. Notice that the node names are added to the nodes data.

from pathlib import Path
from IPython.display import display, Markdown

with Path(destination_file).open() as json_file:
    display(Markdown(f"<pre style='max-height: 160px; overflow: scroll; white-space: pre'>{json_file.read()}</pre>"))

{
  "line":
    [
      {"id": 4, "energized": 1, "loading": 0.15473990756817002, "p_from": 30133.178503728053, "q_from": 10050.721853721689, "i_from": 46.421809438989094, "s_from": 31765.16104977932, "p_to": -29999.9999999991, "q_to": -10000.000000000018, "i_to": 46.42197227045101, "s_to": 31622.776601682945, "id_reference": {"table": "Cables", "key": {"Number": 6}}},
      {"id": 5, "energized": 1, "loading": 0.2946347177166794, "p_from": 60482.835089115535, "q_from": 184.81468538821002, "i_from": 88.39041452675093, "s_from": 60483.11745342729, "p_to": -60000.00000000256, "q_to": -2.981555974335137e-10, "i_to": 88.3904153150038, "s_to": 60000.00000000256, "id_reference": {"table": "Cables", "key": {"Number": 5}}},
      {"id": 6, "energized": 1, "loading": 0.44422983507391905, "p_from": 91713.619010016, "q_from": 10656.105417735627, "i_from": 133.26889142389211, "s_from": 92330.60432266358, "p_to": -90616.01359284009, "q_to": -10235.536539109033, "i_to": 133.26895052217571, "s_to": 91192.25914353265, "id_reference": {"table": "Cables", "key": {"Number": 4}}}
    ],
  "node":
    [
      {"id": 0, "energized": 1, "u_pu": 0.999991421302051, "u": 399.9965685208204, "u_angle": -3.921691889422987e-05, "id_reference": {"table": "Nodes", "key": {"Number": 1}}, "ID": 101, "Name": "First Node"},
      {"id": 1, "energized": 1, "u_pu": 0.983231522194849, "u": 393.2926088779396, "u_angle": -0.003434581394274798, "id_reference": {"table": "Nodes", "key": {"Number": 4}}, "ID": 104, "Name": "Fourth Node"},
      {"id": 2, "energized": 1, "u_pu": 0.9876620765421245, "u": 395.0648306168498, "u_angle": -0.003235042286685305, "id_reference": {"table": "Nodes", "key": {"Number": 2}}, "ID": 102, "Name": "Second Node"},
      {"id": 3, "energized": 1, "u_pu": 0.9797729773054616, "u": 391.9091909221847, "u_angle": -0.006296501518419363, "id_reference": {"table": "Nodes", "key": {"Number": 3}}, "ID": 103, "Name": "Third Node"}
    ],
  "source":
    [
      {"id": 7, "energized": 1, "p": 91713.61901002181, "q": 10656.105417760706, "i": 133.26889142390465, "s": 92330.60432267224, "pf": 0.9933176510954675, "id_reference": {"table": "Sources", "key": {"Node.Number": 1, "Subnumber": 1}}, "Name": "Grid"}
    ],
  "sym_load":
    [
      {"id": 8, "energized": 1, "p": 60000.0, "q": -0.0, "i": 88.39041531500003, "s": 60000.0, "pf": 1.0, "id_reference": {"table": "Loads", "key": {"Node.Number": 3, "Subnumber": 1}}},
      {"id": 9, "energized": 1, "p": 30000.0, "q": 10000.0, "i": 46.421972270452244, "s": 31622.776601683792, "pf": 0.9486832980505139, "id_reference": {"table": "Loads", "key": {"Node.Number": 4, "Subnumber": 1}}}
    ]
}

Other languages

Currently Dutch is the only extra language that is supported for conversion.

%%capture cap --no-stderr

converter = VisionExcelConverter(source_file=source_file, language="nl")

Summary

%%capture cap --no-stderr

from power_grid_model import PowerGridModel, CalculationType
from power_grid_model.validation import assert_valid_input_data
from power_grid_model_io.converters import VisionExcelConverter, PgmJsonConverter

source_file = "data/vision/example.xlsx"
destination_file = "data/vision/sym_output.json"

converter = VisionExcelConverter(source_file=source_file)
input_data, extra_info = converter.load_input_data()
assert_valid_input_data(input_data, calculation_type=CalculationType.power_flow, symmetric=True)
pgm = PowerGridModel(input_data=input_data)
output_data = pgm.calculate_power_flow()
json_converter = PgmJsonConverter(destination_file=destination_file)
json_converter.save(data=output_data, extra_info=extra_info)