
Implementation of a Voltage-Dependent Transmission Line Model with Corona Effect Consideration
Author(s) -
Luis Carlos Timana,
Jaimis S. L. Colqui,
Andres Rodriguez,
Jose Pissolato Filho
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3598646
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This paper presents two models for overhead transmission lines that incorporate the voltage-dependent corona effect directly into the line representation, offering a more accurate representation of the line. The first model is an enhanced version of the Voltage-Dependent Line Model (VDLM), which calculates the shunt conductance and capacitance based on the average voltage across each line section. The second model, referred to as the Alternative Voltage-Dependent Line Model (AVDLM), determines these parameters using the voltages at the ends of the line sections. Both models demonstrate comparable accuracy, high efficiency, and compatibility with EMT-type programs. The models employ a discretization approach, utilizing a cascade connection of interfaces derived from the Bergeron model. Current sources and resistive elements are determined from preceding nodal voltages, effectively capturing the voltage dependence of the shunt capacitance and conductance of the line. The models have been implemented in Matlab and validated against field measurements, demonstrating high accuracy and stability in predicting overvoltage propagation, as well as flexibility in tuning parameters related to the corona effect through the use of a genetic algorithm.
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