Authors :
Eng. Leen Khrais; Eng. Omar Khazaleh
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2yyxn7d9
DOI :
https://doi.org/10.5281/zenodo.8348763
Abstract :
This paper describes a study focused on
enhancing the solar PV efficiency schemes utilising the
MPPT algorithm. The study shows the FLC and ANN
methods for MPPT in addition compares their
performance. It highlights the increasing interest in solar
power and Jordan's efforts to adopt renewable energy
resources. The goal of the study is to develop a cost-
effective MPPT algorithm accomplished by adapting to
varying conditions. The paper describes the
methodology, counting the design of the PV scheme and
simulation parameters. Also, it describes the buck
converter design and offers specifications for the PV
system, buck converter, and NN construction. Simulink
models for the FLC control-based MPPT, and ANN-
based MPPT are obtainable, along with rules of fuzzy
and training process details, respectively. The ultimate
aim is to develop scheme efficiency by using these
algorithms. Accordingly, it can be declared that the
ANN-based MPPT approach trades off the FLC-based
MPPT technique in regards to accuracy, responsiveness,
and total power extraction efficiency based on the
thorough research performed in this work. These results
show the possibility of using ANN in MPPT algorithms
to develop solar system performance and energy
harvesting capacities. Insightful information was
obtained by contrasting the reliability of the FLC-based
MPPT technique with the ANN-based MPPT strategy in
maximising power extraction from solar systems.
Keywords :
Photovoltaic, Maximum Power Point Tracking, Fuzzy Logic Control, Artificial Neural Network, Perturb and Observe Algorithm, Fuzzy Sets, Membership Functions, Fuzzy Rules, Error Histogram, Regression Plot.
This paper describes a study focused on
enhancing the solar PV efficiency schemes utilising the
MPPT algorithm. The study shows the FLC and ANN
methods for MPPT in addition compares their
performance. It highlights the increasing interest in solar
power and Jordan's efforts to adopt renewable energy
resources. The goal of the study is to develop a cost-
effective MPPT algorithm accomplished by adapting to
varying conditions. The paper describes the
methodology, counting the design of the PV scheme and
simulation parameters. Also, it describes the buck
converter design and offers specifications for the PV
system, buck converter, and NN construction. Simulink
models for the FLC control-based MPPT, and ANN-
based MPPT are obtainable, along with rules of fuzzy
and training process details, respectively. The ultimate
aim is to develop scheme efficiency by using these
algorithms. Accordingly, it can be declared that the
ANN-based MPPT approach trades off the FLC-based
MPPT technique in regards to accuracy, responsiveness,
and total power extraction efficiency based on the
thorough research performed in this work. These results
show the possibility of using ANN in MPPT algorithms
to develop solar system performance and energy
harvesting capacities. Insightful information was
obtained by contrasting the reliability of the FLC-based
MPPT technique with the ANN-based MPPT strategy in
maximising power extraction from solar systems.
Keywords :
Photovoltaic, Maximum Power Point Tracking, Fuzzy Logic Control, Artificial Neural Network, Perturb and Observe Algorithm, Fuzzy Sets, Membership Functions, Fuzzy Rules, Error Histogram, Regression Plot.
