Improved Convolutional Neural Network Quantification of Mixed Fault Characterization Gases in Transformers Based on Raman Spectroscopy

CHEN Xin-gang; ZHANG Wen-xuan; MA Zhi-peng; ZHANG Zhi-xian; WAN Fu; AO Yi; ZENG Hui-min

doi:10.3964/j.issn.1000-0593(2025)04-0932-09

Spectroscopy and Spectral Analysis, Volume. 45, Issue 4, 932(2025)

Improved Convolutional Neural Network Quantification of Mixed Fault Characterization Gases in Transformers Based on Raman Spectroscopy

CHEN Xin-gang^1,2, ZHANG Wen-xuan¹, MA Zhi-peng^1、*, ZHANG Zhi-xian¹, WAN Fu³, AO Yi¹, and ZENG Hui-min¹

Author Affiliations

¹School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing400054, China

²Chongqing Energy Internet Engineering Technology Research Center, Chongqing400054, China

³State Key Laboratory of Power Transmission Equipment & System Security and New Technology, (Chongqing University), Chongqing400044, China

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CHEN Xin-gang, ZHANG Wen-xuan, MA Zhi-peng, ZHANG Zhi-xian, WAN Fu, AO Yi, ZENG Hui-min. Improved Convolutional Neural Network Quantification of Mixed Fault Characterization Gases in Transformers Based on Raman Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2025, 45(4): 932

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Paper Information

Received: Jan. 20, 2024

Accepted: Apr. 24, 2025

Published Online: Apr. 24, 2025

The Author Email: MA Zhi-peng (mazhipeng@cqut.edu.cn)

DOI:10.3964/j.issn.1000-0593(2025)04-0932-09

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