Parse MATPOWER case into pandas DataFrame.
Unlike the tutorial on matpower-pip, this package supports parsing MATPOWER case using re instead of Oct2Py and Octave. After that, you can further parse the data into any format supported by your solver.
pip install matpowercaseframes
The main utility of matpowercaseframes is to help read matpower data in user-friendly format as follows,
from matpowercaseframes import CaseFrames
case_path = 'case9.m'
cf = CaseFrames(case_path)
print(cf.gencost)If you have matpower installed via pip install matpower (did not requires matpower[octave]), you can easily navigate matpower case using:
import os
from matpower import path_matpower # require `pip install matpower`
from matpowercaseframes import CaseFrames
case_name = 'case9.m'
case_path = os.path.join(path_matpower, 'data', case_name)
cf = CaseFrames(case_path)
print(cf.gencost)In some cases, a case file may contain matlab code at the end of the file that needs to be executed. An example of such case is case69.m. To properly load this type of file, use the method recommended by matpower, which is using loadcase instead of parsing. To do this, use the load_case_engine parameter (requires matlab or octave), as demonstrated here:
from matpower import start_instance
from matpowercaseframes import CaseFrames
m = start_instance()
case_name = f"case69.m"
cf_lc = CaseFrames(case_name, load_case_engine=m)
cf_lc.branch # see that the branch is already in p.u., converted by `loadcase`Furthermore, matpowercaseframes also support generating data that is acceptable by matpower via matpower-pip package (requires matlab or octave),
from matpowercaseframes import CaseFrames
case_path = 'case9.m'
cf = CaseFrames(case_path)
mpc = cf.to_mpc() # identical with cf.to_dict()
m = start_instance()
m.runpf(mpc)To save all DataFrame to a single xlsx file, use:
from matpowercaseframes import CaseFrames
case_path = 'case9.m'
cf = CaseFrames(case_path)
cf.to_excel('PATH/TO/DIR/case9.xlsx')If you use matpower[octave], CaseFrames also support oct2py.io.Struct as input using:
from matpower import start_instance
from matpowercaseframes import CaseFrames
m = start_instance()
# support mpc before runpf
mpc = m.loadcase('case9', verbose=False)
cf = CaseFrames(mpc)
print(cf.gencost)
# support mpc after runpf
mpc = m.runpf(mpc, verbose=False)
cf = CaseFrames(mpc)
print(cf.gencost)
m.exit()-
This repository was supported by the Faculty of Engineering, Universitas Gadjah Mada under the supervision of Mr. Sarjiya. If you use this package for your research, we would be very glad if you cited any relevant publication under Mr. Sarjiya's name as thanks (but you are not responsible for citing). You can find his publications in the Semantic Scholar or IEEE.
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This repository is working flawlessly with matpower-pip. If you use matpower-pip, make sure to cite using the below citation:
M. Yasirroni, Sarjiya, and L. M. Putranto, "matpower-pip: A Python Package for Easy Access to MATPOWER Power System Simulation Package," [Online]. Available: https://github.com/yasirroni/matpower-pip.
M. Yasirroni, Sarjiya, and L. M. Putranto, "matpower-pip". Zenodo, Jun. 13, 2024. doi: 10.5281/zenodo.11626845.
@misc{matpower-pip, author = {Yasirroni, M. and Sarjiya and Putranto, L. M.}, title = {matpower-pip: A Python Package for Easy Access to MATPOWER Power System Simulation Package}, year = {2023}, howpublished = {\url{https://github.com/yasirroni/matpower-pip}}, } @software{yasirroni_2024_11626845, author = {Yasirroni, Muhammad and Sarjiya, Sarjiya and Putranto, Lesnanto Multa}, title = {matpower-pip}, month = jun, year = 2024, publisher = {Zenodo}, version = {8.0.0.2.1.8}, doi = {10.5281/zenodo.11626845}, url = {\url{https://doi.org/10.5281/zenodo.11626845}}, }
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This package is a fork and simplification from psst MATPOWER parser, thus we greatly thank psst developers and contributors.