### Abstract

This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation, ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system.

Original language | Polish |
---|---|

Pages (from-to) | 116-121 |

Number of pages | 6 |

Journal | Przeglad Elektrotechniczny |

Volume | 88 |

Issue number | 3 B |

Publication status | Published - 2012 |

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### All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering

### Cite this

*Przeglad Elektrotechniczny*,

*88*(3 B), 116-121.

}

*Przeglad Elektrotechniczny*, vol. 88, no. 3 B, pp. 116-121.

**Ulepszona metoda śledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej.** / Younis, Mahmoud A.; Khatib, Tamer; Najeeb, Mushtaq; Mohd Ariffin, Azrul.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Ulepszona metoda śledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej

AU - Younis, Mahmoud A.

AU - Khatib, Tamer

AU - Najeeb, Mushtaq

AU - Mohd Ariffin, Azrul

PY - 2012

Y1 - 2012

N2 - This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation, ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system.

AB - This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation, ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system.

UR - http://www.scopus.com/inward/record.url?scp=84857755382&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84857755382&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84857755382

VL - 88

SP - 116

EP - 121

JO - Przeglad Elektrotechniczny

JF - Przeglad Elektrotechniczny

SN - 0033-2097

IS - 3 B

ER -