A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning

A solid oxide fuel cell (SOFC) is an electrochemical cell that converts chemical energy into electrical energy by oxidizing fuel. SOFC has high efficiency and cleans oxidation residues. Research has shown the importance of SOFC control. Voltage output control is needed because of nonlinearity, slow dynamics, and proper SOFC operating restrictions. This study aims to design an SOFC simulator with output voltage control to optimize the flow rate of fuel (hydrogen) and air (oxygen). This SOFC simulator is designed based on a microcontroller model. The controller is designed using the fuzzy logic method. Tests show that the output voltage can approach the set point with an average of 340.6 volts. The pressure difference (∆Pressure) between the two gases averaged 4428 Pa, and the fuel/gas flow rate was in the range of 0.7 mol/s. The controller can correct both the output voltage of the SOFC simulator and the difference in gas pressure under 8106 Pa (0.08 atm).

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

Applied Energy ◽

10.1016/j.apenergy.2021.116508 ◽

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

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GWO-based MPPT controller for grid connected Solid oxide fuel cell with high step up DC-DC converter

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i3.pp1794-1803 ◽

2021 ◽

Vol 23 (3) ◽

pp. 1794

Author(s):

Sudhakiran Ponnuru ◽

R. Ashok Kumar ◽

N. M. Jothi Swaroopan

Keyword(s):

Fuel Cell ◽

Solid Oxide Fuel Cell ◽

Output Voltage ◽

Solid Oxide ◽

Operating Parameters ◽

Secondary Source ◽

Oxide Fuel ◽

Dynamic Operating ◽

Mppt Controller ◽

Battery Energy

The solid oxide fuel cell (SOFC) is used as secondary source in micro grid application. When the renewable sources are not able meet the load requirement, Battery energy back-up system is supposed to supply the energy to meet the demand. The SOFC come into action when the state of charge (SOC) of battery energy backup becomes too low. The SOFC parameters are assumed as constant during its operation but those operating parameters are not practically constant. The operating parameters vary widely which will influence the output voltage of SOFC. Hence an optimum power extracting controller is being implemented to ensure maximum power under dynamic operating condition. A high step up converter is designed to boost the output voltage of SOFC whose steady state analysis is studied in this work. The switches of high step up converter are triggered using optimum power controller using grey wolf based optimization algorithm. The proposed controller performance is compared with conventional particle swarm optimization based controller. The simulation is carried out using MATLAB/simulink and results discussed.

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

Energies ◽

10.3390/en13164069 ◽

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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