Version 1
: Received: 27 December 2016 / Approved: 27 December 2016 / Online: 27 December 2016 (09:43:52 CET)
How to cite:
Penttonen, J.; Lehtonen, M. Data Driven Analytical Modeling of Power Transformers. Preprints2016, 2016120130. https://doi.org/10.20944/preprints201612.0130.v1
Penttonen, J.; Lehtonen, M. Data Driven Analytical Modeling of Power Transformers. Preprints 2016, 2016120130. https://doi.org/10.20944/preprints201612.0130.v1
Penttonen, J.; Lehtonen, M. Data Driven Analytical Modeling of Power Transformers. Preprints2016, 2016120130. https://doi.org/10.20944/preprints201612.0130.v1
APA Style
Penttonen, J., & Lehtonen, M. (2016). Data Driven Analytical Modeling of Power Transformers. Preprints. https://doi.org/10.20944/preprints201612.0130.v1
Chicago/Turabian Style
Penttonen, J. and Matti Lehtonen. 2016 "Data Driven Analytical Modeling of Power Transformers" Preprints. https://doi.org/10.20944/preprints201612.0130.v1
Abstract
In power systems there are complex transformer structures, whose accurate analysis is not possible using the techniques available today. This paper presents a systematic data driven analysis method for coupled inductors of arbitrary complexity. The method first establishes a winding matrix N mapping the windings to the limbs of the transformer. A permeance matrix P is created from the reluctance network of the magnetic core. A generalized inductance matrix L mapping currents in the transformer windings to the induced voltages is generated based on the winding (N) and permeance (P) matrices. The inductance matrix representation of a coupled inductor is then transformed to an admittance matrix, which can be integrated to the nodal analysis of the electrical circuit surrounding the coupled inductor. The method presented is validated by simulations with real transformer structures using electromagnetic transient program (EMTP/ATP).
Keywords
power transformer; coupled inductor; electro-magnetic modeling
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
