Jump to ContentJump to Main Navigation
Show Summary Details
More options …

International Journal of Emerging Electric Power Systems

Editor-in-Chief: Sidhu, Tarlochan

Ed. by Khaparde, S A / Rosolowski, Eugeniusz / Saha, Tapan K / Gao, Fei

6 Issues per year


CiteScore 2017: 0.86

SCImago Journal Rank (SJR) 2017: 0.186
Source Normalized Impact per Paper (SNIP) 2017: 0.248

Online
ISSN
1553-779X
See all formats and pricing
More options …
Volume 13, Issue 3

Issues

Study of Transformer Switching Overvoltages during Power System Restoration Using Delta-Bar-Delta and Directed Random Search Algorithms

Iman Sadeghkhani / Abbas Ketabi / Rene Feuillet
Published Online: 2012-08-02 | DOI: https://doi.org/10.1515/1553-779X.2996

Abstract

In this paper an intelligent-based approach is introduced to evaluate harmonic overvoltages during three-phase transformer energization. In a power system that appears in an early stage of a black ‎start of a power system, an overvoltage could be caused by core ‎saturation on the energization of a three-phase transformer with residual flux. ‎Such an overvoltage might damage some equipment and delay ‎power system restoration. A new approach based on worst case determination is proposed to reduce time-domain simulations. Also, an artificial neural network (ANN) has been used to estimate the temporary overvoltages (TOVs) due to three-phase transformer ‎energization. ‎ Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. ANN Training is performed based on equivalent circuit parameters of the network; thus trained ANN is applicable to every studied system. The ‎developed ANN is trained with the worst case of the switching condition and remanent flux, and ‎tested for typical cases. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎durations of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

Keywords: artificial neural networks; delta-bar-delta; directed random search; harmonic index; power system restoration; temporary overvoltages; three-phase transformers switching

About the article

Published Online: 2012-08-02


Citation Information: International Journal of Emerging Electric Power Systems, Volume 13, Issue 3, ISSN (Online) 1553-779X, DOI: https://doi.org/10.1515/1553-779X.2996.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in