Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system

Ahmet Afşin KULAKSIZ; Ramazan AKKAYA

Makale detayları

Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system

Yazarlar:

Ahmet Afşin KULAKSIZ | Ramazan AKKAYA

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2012 Vol.20 Issue 2, pp.241-254

Belge Tipi:

Makale

Yayın Tarihi:

1/2012

Belge Dili:

Türkçe

Document ID (Org No.):

2000743614

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Maximum power point tracking (MPPT) algorithms are used to  force photovoltaic (PV) modules to operate at their maximum power  points for all environmental conditions.  In artificial neural network  (ANN)-based algorithms, the maximum power points are acquired by  designing ANN models for PV modules.  However, the parameters of PV  modules are not always provided by the manufacturer and cannot be  obtained readily by the user.  Experimental measurements implemented  in the overall PV system may be used to obtain the ANN dataset.  One  drawback of this method is that the generalization ability of the  neural network usually degrades and some data reducing the  effectiveness of the network may exist.  A genetic algorithm can be  used to automatically select the important data among all the inputs,  resulting in a smaller and more effective dataset.  In our study, a  genetic algorithm is used to improve the MPPT efficiency of a PV  system with induction motor drive by optimizing the input dataset for  an ANN model of PV modules.  A variable frequency volts-per-Hertz  (V/f) control method is applied for speed control of the induction  motor, and a space-vector pulse-width modulation (SV-PWM) method is  used to operate a 3-phase inverter.  Both simulation and experimental  results are presented to demonstrate the validation of the method.

 

Maximum power point tracking (MPPT) algorithms are used to  force photovoltaic (PV) modules to operate at their maximum power  points for all environmental conditions.  In artificial neural network  (ANN)-based algorithms, the maximum power points are acquired by  designing ANN models for PV modules.  However, the parameters of PV  modules are not always provided by the manufacturer and cannot be  obtained readily by the user.  Experimental measurements implemented  in the overall PV system may be used to obtain the ANN dataset.  One  drawback of this method is that the generalization ability of the  neural network usually degrades and some data reducing the  effectiveness of the network may exist.  A genetic algorithm can be  used to automatically select the important data among all the inputs,  resulting in a smaller and more effective dataset.  In our study, a  genetic algorithm is used to improve the MPPT efficiency of a PV  system with induction motor drive by optimizing the input dataset for  an ANN model of PV modules.  A variable frequency volts-per-Hertz  (V/f) control method is applied for speed control of the induction  motor, and a space-vector pulse-width modulation (SV-PWM) method is  used to operate a 3-phase inverter.  Both simulation and experimental  results are presented to demonstrate the validation of the method.

Makale detayları

Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system

Yazarlar: Ahmet Afşin KULAKSIZ | Ramazan AKKAYA

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2012 Vol.20 Issue 2, pp.241-254

Belge Tipi: Makale

Yayın Tarihi: 1/2012

Belge Dili: Türkçe

Document ID (Org No.): 2000743614

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Ahmet Afşin KULAKSIZ | Ramazan AKKAYA

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2012 Vol.20 Issue 2, pp.241-254

Belge Tipi:

Makale

Yayın Tarihi:

1/2012

Belge Dili:

Türkçe

Document ID (Org No.):

2000743614

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632