Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum-Reference-Cited by-同舟云学术

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum

Published:2023 Issue:29 Volume:15 Page:3562-3576
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Yan Pengcheng¹²³,Chen Fengxiang²^ORCID,Kan Xuyue²,Zhang Heng²,Wang Jingbao²,Li Guodong²

Affiliation:

1. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan 232001, China

2. School of Electrical and Information Engineering, Anhui University of Science & Technology, Huainan 232001, China

3. Collaborative Innovation Center of Mine Intelligent Equipment and Technology, Anhui University of Science & Technology, China

Abstract

Accurate diagnosis of transformer faults can effectively improve the enduring reliability of power grid operation.

Funder

Natural Science Foundation of Anhui Province

Anhui University of Science and Technology

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/AY/D3AY00713H

Reference41 articles.

1. Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

2. A Novel Self-Decision Fault Diagnosis Model Based on State-Oriented Correction for Power Transformer

3. Gaussian Process Multi-Class Classification for Transformer Fault Diagnosis Using Dissolved Gas Analysis

4. Power transformer condition assessment based on online monitor with SOFC chromatographic detector

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