PMD Transformer Model Tutorial

Make sure to change runtime to Julia

This repository serves as a comprehensive tutorial for modelling power transformers using PowerModelsDistribution.jl. It is designed to help users bridge the gap between intuitive "Engineering" definitions (such as those in OpenDSS) and the "Mathematical" decomposition used for optimization.

The tutorial covers:

• Transformer Modelling Theory: From basic principles to circuit equivalents.
• OpenDSS Integration: Parsing .dss transformer definitions into Engineering models.
• Mathematical Decomposition: Understanding how n-winding transformers are represented effectively in optimization models.
• Vector Groups: An interactive exploration of phase shifts, connection types (Delta/Wye), and winding permutations.

Access the Tutorial

• 🌐 View HTML Version
  The best way to read the tutorial comfortably in your browser.

• 📓 View Jupyter Notebook
  Access the source notebook to run the Julia code and interact with the vector group simulations locally.

• 💻 View Google Colab Notebook
  Access the notebook in your browser through Google Colab. Make sure to change runtime to Julia.

Using Google Colab to run the tutorial

You can run the tutorial directly in your browser using Google Colab. Just click the "Open in Colab" button at the top of this README. However, make sure to change the runtime type to Julia by following these steps:

from the top dropdown menu, select Change runtime type. Then choose Julia from the Runtime type dropdown menu and click Save.

References

For transformer model implementation details refer to S. Claeys, G. Deconinck and F. Geth, “Decomposition of n-winding transformers for unbalanced optimal power flow,” IET Generation, Transmission & Distribution, vol. 14, no. 24, pp. 5961-5969, 2020, DOI: 10.1049/iet-gtd.2020.0776.

Contribute and raise issues

While great care was put into validating the correctness of the tutorial content, there may still be mistakes or areas for improvement. If you find any issues or have suggestions, please feel free to raise them in the Issues.

Acknowledgement

This work is authored by Mohamed Nuamir part of the strategic research project (IMPROCAP), which is enabled by the support and funding of VLAIO (Agency for Innovation and Entrepreneurship of the Flemish Government) and Flux50 (Grant N°. HBC 2022 0733).