Hybrid Maximum Power Point Tracking Using Artificial Neural Network-Incremental Conduction With Short Circuit Current of Solar Panel

2019 ◽  
Author(s):  
Muhammad Nizar Habibi ◽  
Novie Ayub Windarko ◽  
Anang Tjahjono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Circuit ◽  
Maximum Power Point Tracking ◽  
Short Circuit Current ◽  
Solar Panel ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

scholarly journals Model MPPT Metode Fractional Short Circuit Current Menggunakan JST - AG untuk Solar Panel

2019 ◽  
Vol 1 (01) ◽  
pp. 63-69
Author(s):  
Gunawan Wibisono
Keyword(s):  
Mean Square Error ◽  
Short Circuit ◽  
Maximum Power Point Tracking ◽  
Short Circuit Current ◽  
Solar Panel ◽  
Mean Square ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Sistem photovoltaic memerlukan sebuah metode untuk meningkatkan efisiensi konversinya. Salah satu metodenya adalah menggunakan maximum power point tracking (MPPT). Salah satu metode MPPT, adalah metode fractional short circuit current. Metode ini sediri dapat dioptimalkan lebih jauh menggunakan jaringan syaraf tiruan. Jaringan syaraf tiruan sendiri dapat dilatih menggunakan algoritma genetika. Dengan menggunakan algoritma genetika untuk melatih jaringan syaraf tiruan, didapatkan nilai mean square error (MSE) pelatihan berkisar antara  0,000690983- 0,003210547, dengan rata-rata sebesar 0,002499517.  Sedangkan error pengujian berada pada rentang 8,91%-13,21%.

scholarly journals Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid

2021 ◽  
pp. 57-66
Author(s):  
H. Sahraoui ◽  
H. Mellah ◽  
S. Drid ◽  
L. Chrifi-Alaoui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Artificial Neural ◽  
Power Point

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation – artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

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