Abstract
This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MATLAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems.
| Original language | English |
|---|---|
| Pages (from-to) | 29-38 |
| Number of pages | 10 |
| Journal | Energy and Buildings |
| Volume | 51 |
| DOIs | |
| Publication status | Published - Aug 2012 |
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Keywords
- Fuzzy logic
- Hopfield neural network
- Maximum power point tracking
- Photovoltaic
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Mechanical Engineering
- Electrical and Electronic Engineering
Cite this
Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller. / Subiyanto, Subiyanto; Mohamed, Azah; M A, Hannan.
In: Energy and Buildings, Vol. 51, 08.2012, p. 29-38.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller
AU - Subiyanto, Subiyanto
AU - Mohamed, Azah
AU - M A, Hannan
PY - 2012/8
Y1 - 2012/8
N2 - This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MATLAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems.
AB - This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MATLAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems.
KW - Fuzzy logic
KW - Hopfield neural network
KW - Maximum power point tracking
KW - Photovoltaic
UR - http://www.scopus.com/inward/record.url?scp=84860588364&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860588364&partnerID=8YFLogxK
U2 - 10.1016/j.enbuild.2012.04.012
DO - 10.1016/j.enbuild.2012.04.012
M3 - Article
AN - SCOPUS:84860588364
VL - 51
SP - 29
EP - 38
JO - Energy and Buildings
JF - Energy and Buildings
SN - 0378-7788
ER -