LAPSE:2023.25705
Published Article
LAPSE:2023.25705
Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven
Xiaowei Fu, Yanlin Liu, Xi Li
March 29, 2023
The solid oxide fuel cell (SOFC) is a new energy technology that has the advantages of low emissions and high efficiency. However, oscillation and propagation often occur during the power generation of the system, which causes system performance degradation and reduced service life. To determine the root cause of multi-loop oscillation in an SOFC system, a data-driven diagnostic method is proposed in this paper. In our method, kernel principal component analysis (KPCA) and transfer entropy were applied to the system oscillation fault location. First, based on the KPCA method and the Oscillation Significance Index (OSI) of the system process variable, the process variables that were most affected by the oscillations were selected. Then, transfer entropy was used to quantitatively analyze the causal relationship between the oscillation variables and the oscillation propagation path, which determined the root cause of the oscillation. Finally, Granger causality (GC) analysis was used to verify the correctness of our method. The experimental results show that the proposed method can accurately and effectively locate the root cause of the SOFC system’s oscillation.
Keywords
data-driven, Granger causality, kernel principal component analysis, oscillation root cause diagnosis, solid oxide fuel cell, transfer entropy
Suggested Citation
Fu X, Liu Y, Li X. Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven. (2023). LAPSE:2023.25705
Author Affiliations
Fu X: College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China; State Key Laboratory of
Liu Y: College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China
Li X: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Journal Name
Energies
Volume
13
Issue
16
Article Number
E4069
Year
2020
Publication Date
2020-08-06
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en13164069, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.25705
This Record
External Link

doi:10.3390/en13164069
Publisher Version
Download
Files
[Download 1v1.pdf] (1.2 MB)
Mar 29, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
65
Version History
[v1] (Original Submission)
Mar 29, 2023
 
Verified by curator on
Mar 29, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.25705
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version