Open AccessNon‐linear characteristic quantity extraction of ferroresonance overvoltage time series
Author(s) -
Yang Ming,
Sima Wenxia,
Yang Qing,
Li Jianbiao,
Zou Mi,
Duan Qichang
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0873
Subject(s) - ferroresonance in electricity networks , overvoltage , control theory (sociology) , transformer , lyapunov exponent , nonlinear system , computer science , mathematics , voltage , engineering , artificial intelligence , physics , electrical engineering , control (management) , quantum mechanics
Ferroresonance is a common and complex phenomenon in power systems. Some types of ferroresonances are remarkably difficult to distinguish from others. This study adopts non‐linear analysis methods to directly extract non‐linear characteristic quantities from ferroresonance overvoltage time series (FOTS). Simulated and practical FOTS are investigated to validate the proposed method. Non‐linear characteristic quantities, i.e. average grey value of reconstructed attractor and improved largest Lyapunov exponent of ferroresonance overvoltage, are extracted based on phase space reconstruction of voltage time series to identify different types of ferroresonance. Practical FOTS acquired from a transformer substation is used to validate the proposed method. Non‐linear characteristic quantities obtained in this study can be applied to identify different types of ferroresonance. These characteristics allow further understanding of the non‐linear ferroresonance phenomenon from another point of view and benefit the study of ferroresonance identification and suppression.