Optimization of Biogas Electrical Power Generation using Neuro-Fuzzy Controller


  • Araoye Timothy Oluwaseun
  • Alor Michael Onyeamaechi 1Department of Electrical and Electronics Engineering, Enugu State University of Science and Technology, Enugu, Nigeria.
  • Okika Stephen Sunday Department of Electrical and Electronics Engineering, Enugu State University of Science and Technology, Enugu, Nigeria.




Biogas power generation, MATLAB/SIMULINK, Neuro-fuzzy controller, Renewable energy


Biogas electrical power generation is a renewable energy which originated from biological materials. The technology design and model power system that predict and control the generation of biogas Electrical production. This research paper develops a Neuro-fuzzy controller model for generation of Biogas power production. A Neuro-fuzzy controller is design to the Biogas power system in order to improve the power quality delivery to the load. The set of 27 rules are written for proper training of biogas electrical data in the neural network. The training is used to control signal of the Biogas Power output of the system. The  output  of  Neural Network  unit  is  given  as  input  to  the de-fuzzification  unit and the linguistic variables are converted back into the crisp form. Therefore the algorithm was designed to decide power supply to the load as to improve the performance of the biogas system using MATLAB/SIMULINK and Neuro-fuzzy model was developed for easy input of the data. The result shows that biogas electrical power output increased by 4.39kw, which is 54.8% increase when Neuro-fuzzy controller is incorporated. The improvement in the system is due to the training of input parameters of the biogas generated. The result obtained shows that there is Real Power improvement in Biogas system when Neuro-fuzzy is incorporated in the system model


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How to Cite

Oluwaseun, A. T. ., Onyeamaechi, A. M. ., & Sunday, O. S. . (2020). Optimization of Biogas Electrical Power Generation using Neuro-Fuzzy Controller. Transactions on Machine Learning and Artificial Intelligence, 7(6), 21–29. https://doi.org/10.14738/tmlai.76.7239