Dynamic and Performance Analysis of Distributed Hybrid Photovoltaic (PV) and Fuel Cell based Power Generation using Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
P. Radhakrishnan
Keyword(s):  
Power Generation ◽  
Fuel Cell ◽  
Performance Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
And Performance

scholarly journals Dynamic Modeling of a Reformed Methanol Fuel Cell System Using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Kristian K. Justesen ◽  
Søren Juhl Andreasen ◽  
Hamid Reza Shaker
Keyword(s):  
Fuel Cell ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cell Model ◽  
Empirical Methods ◽  
Inference System ◽  
System Models ◽  
Neuro Fuzzy ◽  
The Individual ◽  
Methanol Fuel Cell

In this work, a dynamic matlab Simulink model of an H3-350 reformed methanol fuel cell (RMFC) stand-alone battery charger produced by Serenergy® is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous hydrogen, which is difficult and energy-consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and adaptive neuro-fuzzy inference system models of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other's output. The models take this into account using an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model, which is adapted to fit the measured performance of the H3-350 module. All of the individual parts of the model are verified and fine-tuned through a series of experiments and are found to have mean absolute errors between 0.4% and 6.4% but typically below 3%. After a comparison between the performance of the combined model and the experimental setup, the model is deemed to be valid for control design and optimization purposes.

Adaptive neuro-fuzzy inference system to improve the power quality of a split shaft microturbine power generation system

2012 ◽  
Vol 197 ◽  
pp. 196-209 ◽  
Author(s):  
Yüksel Oğuz ◽  
Seydi Vakkas Üstün ◽  
İsmail Yabanova ◽  
Mehmet Yumurtaci ◽  
İrfan Güney
Keyword(s):  
Power Generation ◽  
Power Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Generation System ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Power Generation System

scholarly journals Prediction of strength and durability properties of HPC composites using Adaptive Neuro-fuzzy Inference System

2020 ◽  
Vol 184 ◽  
pp. 01102
Author(s):  
P Magudeaswaran. ◽  
C. Vivek Kumar ◽  
Rathod Ravinder
Keyword(s):  
Fuzzy Inference System ◽  
High Performance ◽  
Fuzzy Inference ◽  
High Performance Concrete ◽  
Intelligent System ◽  
Computing Methods ◽  
Inference System ◽  
Performance Requirements ◽  
Neuro Fuzzy ◽  
And Performance

High-Performance Concrete (HPC) is a high-quality concrete that requires special conformity and performance requirements. The objective of this study was to investigate the possibilities of adapting neural expert systems like Adaptive Neuro-Fuzzy Inference System (ANFIS) in the development of a simulator and intelligent system and to predict durability and strength of HPC composites. These soft computing methods emulate the decision-making ability of human expert benefits both the construction industry and the research community. These new methods, if properly utilized, have the potential to increase speed, service life, efficiency, consistency, minimizes errors, saves time and cost which would otherwise be squandered using the conventional approaches.

scholarly journals Dynamic Modeling of a Reformed Methanol Fuel Cell System Using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

2013 ◽  
Author(s):  
Kristian K. Justesen ◽  
Søren J. Andreasen ◽  
Hamid R. Shaker
Keyword(s):  
Fuel Cell ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cell Model ◽  
Empirical Methods ◽  
Inference System ◽  
System Models ◽  
Neuro Fuzzy ◽  
The Individual ◽  
Methanol Fuel Cell

In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy® is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous hydrogen, which is difficult and energy consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and Adaptive Neuro-Fuzzy Inference System models of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified and fine-tuned through a series of experiments and are found to have mean absolute errors between 0.4% and 6.4% but typically below 3%. After a comparison between the performance of the combined model and the experimental setup, the model is deemed to be valid for control design and optimization purposes.

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