ModelicaGridData: Massive power system simulation data generation and labeling tool using Modelica and Python

Sergio A. Dorado-Rojas; Fernando Fachini; Tetiana Bogodorova; Giuseppe Laera; Marcelo de Castro Fernandes; Luigi Vanfretti

doi:10.1016/j.softx.2022.101258

ModelicaGridData: Massive power system simulation data generation and labeling tool using Modelica and Python

Sergio A. Dorado-Rojas, Fernando Fachini, Tetiana Bogodorova, Giuseppe Laera, Marcelo de Castro Fernandes, Luigi Vanfretti

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

This paper describes tool that is created for massive data generation employing phasor time-domain Modelica simulations and using the Open-Instance Power System Library (OpenIPSL). provides a pipeline for generating large amounts of data, considering a wide range of operating conditions and potential contingencies experienced by a power system. The need for large-scale power system dynamic data arises with the development of Machine Learning (ML) solutions in the context of the modernization of the existing power grid. implements algorithms to process different types of input data, perform steady-state computations, run dynamic simulations and linear analysis routines, and label the resulting data sets. The tool has been developed entirely in Python 3 and is compatible with the Modelica IDEs - Dymola and OpenModelica.

Original language	English (US)
Pages (from-to)	101258
Journal	SoftwareX
Volume	21
DOIs	https://doi.org/10.1016/j.softx.2022.101258
State	Published - Dec 9 2022
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2023-01-23
Acknowledgements: This work was funded in part by the New York State Energy Research and Development Authority (NYSERDA), USA under grant agreement numbers 137951 and 137940, and in part by the Center of Excellence for NEOM Research at King Abdullah University of Science and Technology, Saudi Arabia . The authors would like to thank Aisling Pigott for the discussions that helped shape the final version of the tool, and Santiago Peñate for making available as open-source software and for answering questions about its usage during the development of this tool.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

Software
Computer Science Applications

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10.1016/j.softx.2022.101258

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title = "ModelicaGridData: Massive power system simulation data generation and labeling tool using Modelica and Python",

abstract = "This paper describes tool that is created for massive data generation employing phasor time-domain Modelica simulations and using the Open-Instance Power System Library (OpenIPSL). provides a pipeline for generating large amounts of data, considering a wide range of operating conditions and potential contingencies experienced by a power system. The need for large-scale power system dynamic data arises with the development of Machine Learning (ML) solutions in the context of the modernization of the existing power grid. implements algorithms to process different types of input data, perform steady-state computations, run dynamic simulations and linear analysis routines, and label the resulting data sets. The tool has been developed entirely in Python 3 and is compatible with the Modelica IDEs - Dymola and OpenModelica.",

author = "Dorado-Rojas, {Sergio A.} and Fernando Fachini and Tetiana Bogodorova and Giuseppe Laera and Fernandes, {Marcelo de Castro} and Luigi Vanfretti",

note = "KAUST Repository Item: Exported on 2023-01-23 Acknowledgements: This work was funded in part by the New York State Energy Research and Development Authority (NYSERDA), USA under grant agreement numbers 137951 and 137940, and in part by the Center of Excellence for NEOM Research at King Abdullah University of Science and Technology, Saudi Arabia . The authors would like to thank Aisling Pigott for the discussions that helped shape the final version of the tool, and Santiago Pe{\~n}ate for making available as open-source software and for answering questions about its usage during the development of this tool. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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doi = "10.1016/j.softx.2022.101258",

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AU - Laera, Giuseppe

AU - Fernandes, Marcelo de Castro

AU - Vanfretti, Luigi

N1 - KAUST Repository Item: Exported on 2023-01-23 Acknowledgements: This work was funded in part by the New York State Energy Research and Development Authority (NYSERDA), USA under grant agreement numbers 137951 and 137940, and in part by the Center of Excellence for NEOM Research at King Abdullah University of Science and Technology, Saudi Arabia . The authors would like to thank Aisling Pigott for the discussions that helped shape the final version of the tool, and Santiago Peñate for making available as open-source software and for answering questions about its usage during the development of this tool. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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ModelicaGridData: Massive power system simulation data generation and labeling tool using Modelica and Python

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