A Fault Diagnosis Method of Oil-Immersed Transformer Based on Improved Harris Hawks Optimized Random Forest-Reference-Cited by-同舟云学术

A Fault Diagnosis Method of Oil-Immersed Transformer Based on Improved Harris Hawks Optimized Random Forest

Published:2022-03-22 Issue:4 Volume:17 Page:2527-2540
ISSN:1975-0102
Container-title:Journal of Electrical Engineering & Technology
language:en
Short-container-title:J. Electr. Eng. Technol.

Author:

Yi Lingzhi,Jiang Ganlin^ORCID,Zhang Guoyong,Yu Wenxin,Guo You,Sun Tao

Funder

the National Natural Science Foundation of China

Key Laboratory Open Project Fund of Disaster Prevention and Mitigation for Power Grid Transmission and Transformation Equipment

Hunan province Natural science Zhuzhou United foundation

Postgraduate Scientific Research Innovation Project of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42835-022-01036-z.pdf

Reference23 articles.

1. Zhang YW, Feng B, Chen Y et al (2021) Fault diagnosis method for oil-immersed transformer based on XGBoost optimized by genetic algorithm. Electr Power Autom Equip 41(02):200–206

2. Du HP, Wang G, Li JZ (2021) Transformer fault identification with an IF-1DCNN based on informative integration of heterogeneous sources. Math Probl Eng 2021:1–14

3. Lu SJ, Gao W, Hong C et al (2021) A newly-designed fault diagnostic method for transformers via improved empirical wavelet transform and kernel extreme learning machine. Adv Eng Inf 49:101320

4. Faiz J, Soleimani M (2018) Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis. IEEE Trans Dielectr Electr Insul 25(5):1798–1806

5. Wani SA, Khan SA, Prashal G et al (2019) Smart diagnosis of incipient faults using dissolved gas analysis-based fault interpretation matrix (FIM). Arab J Sci Eng 44:6977–6985

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