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

被引:4
|
作者
Dorado-Rojas, Sergio A. [1 ,2 ]
Fachini, Fernando [1 ]
Bogodorova, Tetiana [1 ]
Laera, Giuseppe [1 ]
Fernandes, Marcelo de Castro [1 ]
Vanfretti, Luigi [1 ]
机构
[1] Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA
[2] Univ Connecticut, Dept Elect & Comp Engn, Storrs, CT USA
关键词
Big data generation; OpenIPSL; Modelica; Power systems; Small-signal stability; Power flow;
D O I
10.1016/j.softx.2022.101258
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This paper describes ModelicaGridData tool that is created for massive data generation employing phasor time-domain Modelica simulations and using the Open-Instance Power System Library (OpenIPSL). ModelicaGridData 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. ModelicaGridData 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. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页数:8
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