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503 results on '"Chen, WeiGen"'

22. Analysis and Diagnosis of Generator Step-Up Transformer Faults

Author

Li, Zhijun, Shan, Jie, Chang, Cheng-Kuo, Chen, Weigen, Cao, LingYan, Yang, Zhiyong, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Pan, Jeng-Shyang, editor, Li, Jianpo, editor, Ryu, Keun Ho, editor, Meng, Zhenyu, editor, and Klasnja-Milicevic, Aleksandra, editor

Published
2021
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24. Ice-template-induced highly ionic conductive PVA/PEG-SiO2 gel polymer electrolyte for zinc-ion batteries.

Author

Wan, Fu, Hu, Kaida, Liu, Ruiqi, Zhang, Sida, Li, Shufan, Lei, Yu, Yang, Da, Wang, Changding, Xia, Yaoyang, and Chen, Weigen

Subjects
POLYELECTROLYTES, POLYMER colloids, IONIC conductivity, ZINC ions, POLYVINYL alcohol, FLEXIBLE electronics, POLYETHYLENE glycol, WEARABLE technology
Abstract

In this work, a SiO2 doped polyvinyl alcohol/polyethylene glycol (PVA/PEG) gel polymer electrolyte (PVA/PEG-SiO2) was constructed via an ice-crystal template for zinc-ion batteries. The SiO2 and the three-dimensional porous skeleton make it have excellent ionic conductivity and mechanical strength, and inhibit the growth of dendrites. The assembled ZIBs exhibit excellent rate performance and cycle stability, making it a promising electrolyte membrane candidate for flexible wearable electronics. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Raman spectrum feature extraction and diagnosis of oil–paper insulation ageing based on kernel principal component analysis

Author

Dingkun Yang, Chen Weigen, Shi Haiyang, Wan Fu, and Zhou Yongkuo

Subjects
Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Electricity, QC501-721
Abstract

Abstract Raman spectroscopy, with its specific ability to generate a unique fingerprint‐like spectrum of certain substances, has attracted much attention in diagnosing the ageing degree of oil–paper insulation. In this study, the feature extraction and ageing diagnosis methods of oil–paper insulation Raman spectroscopy data are further studied. Based on the non‐linear analysis of Raman spectra of different ageing samples, kernel principal component analysis was applied to extract the spectral features, and the back‐propagation neural network was used to build a diagnosis model with high diagnostic accuracy. The results show that Raman spectroscopy combined with kernel principal component analysis and the back‐propagation neural network can diagnose the ageing state of oil–paper insulation, with a diagnostic accuracy of 91.43% (64/70). The proposed method provides an effective and feasible method for the ageing assessment of oil‐immersed electrical equipment.

Published
2021
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46. Review of Digital Vibration Signal Analysis Techniques for Fault Diagnosis of High-Voltage Circuit Breakers

Author

Tan, Yaxiong, Hu, Erhan, Liu, Yuan, Li, Jian, and Chen, Weigen

Abstract

This article provides an in-depth review of recent research into high-voltage circuit breaker (HVCB) fault diagnosis practices that use digital vibration signal analysis technologies. HVCB vibration signals provide a wealth of mechanical condition data, allowing for real-time, noninvasive, and comprehensive mechanical fault (MF) diagnosis. However, because HVCB vibration signals are typically nonlinear, nonperiodic, and transient, precisely extracting fault features and identifying fault types is difficult. The rapid development of digital analysis techniques has opened up new avenues for solving this problem. First, three crucial stages in fault diagnosis—vibration data acquisition, feature extraction, and fault identification—are introduced and analyzed. The data acquisition platform construction, signal acquisition process, and corresponding parameters are briefly introduced, including multisensor information fusion fault identification methods. Furthermore, the advantages and disadvantages, similarities, and differences of mechanistic, machine learning (ML), and deep learning (DL) approaches are examined. Next, existing difficulties in the field are described, and the solutions presented by recent studies are discussed, including imbalanced data training, fault degree identification, and noise immunity. Finally, we summarize and provide a research outlook; this work should serve as a useful guide for researchers developing HVCB diagnosis and online monitoring procedures based on vibration signals.

Published
2024
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47. Distributed Partial Discharge Acoustic Signal Detection and Localization Technology for GIL With Built-in Fiber Optics

Author

Song, Yuxuan, Tian, Haoyuan, Jia, Zongqing, Chen, Weigen, Zhang, Weikai, Wang, Changding, Zhou, Tao, Li, Litong, Hong, Deng, Wang, Yan, Liu, Qingchun, and Liu, Hong

Abstract

Fiber optic distributed acoustic sensing (DAS) is a potential method to achieve partial discharge (PD) detection and fast localization in gas insulated metal enclosed transmission lines (GIL). In order to detect and locate the weak partial discharge signals in the GIL as early as possible, this paper proposes an integrated distributed fiber optic sensor installation method for the GIL, as well as a distributed partial discharge acoustic detection and localization system. Based on the fully insulating nature of the optical fiber, the optical fiber is installed inside the GIL to improve the sensitivity to partial discharge acoustic signals, and the length of the sensor unit is increased by repeated winding to further improve the detection performance of the acoustic signals. A true-to-life GIL partial discharge test platform was built to investigate the multi-point distributed sensing performance of the proposed coherent optical time domain reflectometer system for metal tip discharge. The test results show that the sensing distance of the sensing system can reach 120m, which can achieve the multipoint detection and localization of the partial discharge acoustic signal of 29.7 pC. The proposed sensing method and system provide a new solution for GIL weak partial discharge optical fiber distributed acoustic signal detection.

Published
2024
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