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CORRECTION article

Front. Energy Res., 27 June 2023
Sec. Smart Grids
Volume 11 - 2023 | https://doi.org/10.3389/fenrg.2023.1232841
This article is part of the Research Topic High Quality and Reliability of Transformers View all 8 articles

Corrigendum: Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics

This article is a correction to:

  1. Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics

    1. Read original article
Xiu ZhouXiu Zhou1Yan LuoYan Luo1Tian Tian Tian Tian1*Haonan BaiHaonan Bai2Peng WuPeng Wu3Weifeng LiuWeifeng Liu1
  • 1State Grid Ningxia Electric Power Co., Ltd., Electric Power Research Institute, Yinchuan, China
  • 2School of Electrical Engineering, Shenyang University of Technology, Shengyang, China
  • 3Maintenance Company of State Grid Ningxia Electric Power Co., Ltd., Yinchuan, China

A Corrigendum on
Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics

by Zhou X, Luo Y, Tian T, Bai H, Wu P and Liu W (2023). Front. Energy Res. 11:1169508. doi: 10.3389/fenrg.2023.1169508

In the published article, there was an error in the Funding statement. The sentence “Science and Technology Project of State Grid Ningxia Electric Power Co., Ltd. (5229DK200051)” is incorrect. The correct sentence appears below.

“Science and Technology Project of State Grid Ningxia Electric Power Co., Ltd. (5229DK20004N).”

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: transformer, vibration and noise, probabilistic neural networks, DC bias, multi-point grounding, short-circuit between silicon steel sheets, fault diagnosis

Citation: Zhou X, Luo Y, Tian T, Bai H, Wu P and Liu W (2023) Corrigendum: Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics. Front. Energy Res. 11:1232841. doi: 10.3389/fenrg.2023.1232841

Received: 01 June 2023; Accepted: 21 June 2023;
Published: 27 June 2023.

Approved by

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2023 Zhou, Luo, Tian, Bai, Wu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tian Tian, dGlhbnQwNTMxQDE2My5jb20=

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.