A novel data-driven controller for solid oxide fuel cell via deep reinforcement learning

2021 ◽  
pp. 128929
Author(s):  
Jiawen Li ◽  
Tao Yu
Keyword(s):  
Reinforcement Learning ◽  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Solid Oxide ◽  
Data Driven ◽  
Oxide Fuel

Data-driven nonlinear control of a solid oxide fuel cell system

2012 ◽  
Vol 19 (7) ◽  
pp. 1892-1901 ◽  
Author(s):  
Yi-guo Li ◽  
Jiong Shen ◽  
K. Y. Lee ◽  
Xi-chui Liu ◽  
Wen-zhe Fei
Keyword(s):  
Fuel Cell ◽  
Nonlinear Control ◽  
Solid Oxide Fuel Cell ◽  
Cell System ◽  
Solid Oxide ◽  
Data Driven ◽  
Oxide Fuel ◽  
Fuel Cell System

Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage

2021 ◽  
Vol 286 ◽  
pp. 116508
Author(s):  
Yuan-wu Xu ◽  
Xiao-long Wu ◽  
Xiao-bo Zhong ◽  
Dong-qi Zhao ◽  
Marco Sorrentino ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Solid Oxide ◽  
Data Driven ◽  
Oxide Fuel ◽  
Fuel Cell Stack ◽  
Mechanism Model ◽  
Model Based ◽  
Data Driven Approach

scholarly journals Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4069
Author(s):  
Xiaowei Fu ◽  
Yanlin Liu ◽  
Xi Li
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
High Efficiency ◽  
Solid Oxide ◽  
Transfer Entropy ◽  
Data Driven ◽  
Propagation Path ◽  
Kernel Principal Component Analysis ◽  
Oxide Fuel ◽  
Root Cause

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.

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