Solid oxide fuel cell (SOFC) control strategy enhancement by adaptive neuro-fuzzy inference system (ANFIS)

Sameh Ramadan; Mostafa Al-Gabalaw; Mohamed El-Shimy; Adel Emarah

doi:10.1038/s41598-024-62414-3

Solid oxide fuel cell (SOFC) control strategy enhancement by adaptive neuro-fuzzy inference system (ANFIS)

Sci Rep. 2024 Aug 2;14(1):17911. doi: 10.1038/s41598-024-62414-3.

Authors

Sameh Ramadan¹, Mostafa Al-Gabalaw², Mohamed El-Shimy³, Adel Emarah³

Affiliations

¹ Electric Power and Machines Department, Ain Shams University, Cairo, Egypt. 2002064@eng.asu.edu.eg.
² Electric Power Engineering Department, Suez University, Suez, Egypt.
³ Electric Power and Machines Department, Ain Shams University, Cairo, Egypt.

Abstract

Global warming is a vital problem that many researchers tried to solve with so many solutions such as: reducing electricity production with conventional generators by using renewable energy resources and using hydrogen as an alternative to fossil fuels. The universal Economic crisis came across to cut off certain quantities at scheduled times. This, directly, affects renewable energy sources connected to the grid due to voltage and frequency variations. To solve this dilemma, in this paper, the grid-connected solid oxide fuel cell (SOFC) model which is fed by green hydrogen to produce AC power, is developed by an Adaptive neuro-fuzzy inference system (ANFIS). It is one of the artificial intelligence applications to improve grid-connected SOFC dynamic response. ANFIS is mathematically presented and simulated using MATLAB/SIMULINK. The results show that the ANFIS controller has succeeded in enhancing most of the desired control and operation signals.