Treffer: Investigating Ferroresonance Susceptibility in Various Transformer Configurations: A Simulation‐Based Study.

Ferroresonance poses a major threat to the quality and reliability of power distribution systems due to its inherent characteristics of sustained overvoltages and currents. This paper aims to enhance the understanding and reduce the ferroresonance threat by investigating the susceptibility of different transformer configurations using MATLAB/Simulink simulations. To achieve this, four 200 kVA transformers with different vector groups (D11‐Yn, Yg‐Yg, Yn‐Yn, and Y‐D11) and core types (3‐limb and 5‐limb) were systematically exposed to controlled ferroresonance conditions. The influence of varying the length of the 11 kV cable connected to the transformers was also examined. Unlike previous studies, which primarily relied on waveform analysis, our approach integrates total harmonic distortion of voltage (THDv), total harmonic distortion of current (THDi), peak overvoltage, peak current, and energy content analysis of the ferroresonance oscillations. This methodology facilitates a more rigorous and comparative evaluation of transformer susceptibility, equipping utilities and manufacturers with practical tools to assess and mitigate ferroresonance risks in real‐world applications. The findings indicate that the Y‐D11 configurations exhibited lower susceptibility to ferroresonance than the others. It was also observed that ferroresonance effects are most pronounced within a cable length range of 1.5 km–2 km, beyond which the distributed capacitance helps to moderate the severity. A key contribution of this research is the development of a multimetric ferroresonance susceptibility framework. This framework advances beyond traditional qualitative assessments by providing a data‐driven methodology for evaluating transformer vulnerability. [ABSTRACT FROM AUTHOR]

Copyright of International Transactions on Electrical Energy Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)