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An Open Source Power System Simulator in Python for Efficient Prototyping of WAMPAC Applications

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arxiv 2101.02937 v1 pith:BO6HSSBH submitted 2021-01-08 eess.SY cs.SY

classification eess.SYcs.SY
keywords simulationpowercodepythonsystemapplicationsopenprototyping
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An open source software package for performing dynamic RMS simulation of small to medium-sized power systems is presented, written entirely in the Python programming language. The main objective is to facilitate fast prototyping of new wide area monitoring, control and protection applications for the future power system by enabling seamless integration with other tools available for Python in the open source community, e.g. for signal processing, artificial intelligence, communication protocols etc. The focus is thus transparency and expandability rather than computational efficiency and performance. The main purpose of this paper, besides presenting the code and some results, is to share interesting experiences with the power system community, and thus stimulate wider use and further development. Two interesting conclusions at the current stage of development are as follows: First, the simulation code is fast enough to emulate real-time simulation for small and medium-size grids with a time step of 5 ms, and allows for interactive feedback from the user during the simulation. Second, the simulation code can be uploaded to an online Python interpreter, edited, run and shared with anyone with a compatible internet browser. Based on this, we believe that the presented simulation code could be a valuable tool, both for researchers in early stages of prototyping real-time applications, and in the educational setting, for students developing intuition for concepts and phenomena through real-time interaction with a running power system model.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework

    eess.SY 2026-08 conditional novelty 4.0 of 10

    A survey-motivated, tiered library of six executable notebooks teaches AI on power-system tasks, with demand and webinar attendance as early evidence.

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