Next Article in Journal
An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning
Previous Article in Journal
Asymptotic Profiles and Convergence Rates of the Linearized Compressible Navierâ€“Stokesâ€“ Korteweg System

Metrics 0

## Export Article

Open AccessArticle
Mathematics 2019, 7(3), 288; https://doi.org/10.3390/math7030288

# Novel Transformer Fault Identification Optimization Method Based on Mathematical Statistics

1
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China
2
Quzhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Quzhou 324002, China
*
Author to whom correspondence should be addressed.
Received: 7 December 2018 / Revised: 13 March 2019 / Accepted: 15 March 2019 / Published: 21 March 2019
|
PDF [2256 KB, uploaded 21 March 2019]
|

# Abstract

Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification. View Full-Text
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

MDPI and ACS Style

Zhang, Z.; Wu, Y.; Zhang, R.; Jiang, P.; Liu, G.; Ahmed, S.; Dong, Z. Novel Transformer Fault Identification Optimization Method Based on Mathematical Statistics. Mathematics 2019, 7, 288.

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

1