A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only

Abstract

This paper proposes an algorithm based on discrete wavelet transform (DWT) for discriminating among inrush current, internal fault, and external fault in power transformers. Fault conditions are simulated using the Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP). Daubechies4 (db4) is employed as the mother wavelet to decompose low‐frequency components from fault signals. The ratio between per unit (p.u.) differential current and p.u. time is suggested as an index. The numerator of the ratio is the difference between the maximum differential current and the minimum differential current in terms of p.u. with a base value selected at the transformer‐rated current. The ratio is calculated for all three phases, and from a trial and error process the indices for the separation among the internal fault condition, the external fault condition, and inrush condition are defined. The results obtained from the proposed technique show good accuracy for discriminating faults in the considered system. In addition, the proposed algorithm uses data of the differential current with a time of quarter cycle under the analysis. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Transformer Internal and Inrush Current Fault Detection Using Machine Learning

“…Therefore, monitoring the temperature of the breather is also considered an essential step toward transformer maintenance. The protection system for the transformer is incorporated using differential relays [2]. The output of the current transformer is fed into the relay, where under normal conditions, the transformer primary and the secondary CTs generate the same amount of current on the secondary side of the CTs.…”