scholarly journals Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

2019 ◽  
Vol 5 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Z. Zandi ◽  
A. H. Mazinan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plants ◽  
Solar Power ◽  
Maximum Power Point Tracking ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Solar Power Plants ◽  
Power Point

Implementaasi Maximum Power Point Tracker (MPPT) dengan topologi sepic pada pembangkit listrik tenaga surya

JURNAL ELTEK ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 1
Author(s):  
Oktriza Melfazen ◽  
M. Taqijuddin Alawiy ◽  
Denda Dewatama
Keyword(s):  
Power Plants ◽  
Solar Power ◽  
Maximum Power Point Tracking ◽  
Average Power ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Arduino Uno ◽  
Power Point

Terdapat rugi-rugi daya dalam proses menghasilkan daya pada Pembangkit Listrik Tenaga Surya (PLTS) konvensional. Sehingga energi yang dihasilkan tidak terserap secara maksimal. Sistem Pembangkit Listrik Tenaga Surya yang didesain dalam penelitian ini diharapkan dapat menghasilkan energi optimal dengan memanfaatkan kemampuan algoritma Maximum Power Point Tracking (MPPT) dengan metode Perturb and Obserb yang diaplikasikan pada topologi SEPIC. Pada penelitian ini, sistem  menggunakan panel surya berjenis amorphous 60W, sensor arus ACS712, sensor tegangan berupa pembagi tegangan dan rangkaian converter dengan topologi SEPIC yang dikontrol mikrokontroler Arduino UNO dengan sistem MPPT. Hasil penelitian yang didapat sebagai berikut: penempatan panel surya yang baik adalah menghadap atas (tegak lurus dengan permukaan bumi, sensor arus bekerja dengan eror rata-rata 1,92%, sensor tegangan mempunyai eror rata-rata 2,76%, dan topologi SEPIC dengan MPPT mempunyai hasil daya rata-rata 26,13 W.   There are power losses in the process of generating power in conventional Solar Power Plants (PLTS). So that the energy produced is not absorbed to the fullest. The Solar Power Sistem designed in this study is expected to produce optimal energy by utilizing the ability of the Maximum Power Point Tracking (MPPT) algorithm with the Perturb and Obserb method applied to the SEPIC topology. The sistem built in this study uses a 60W amorphous type solar panel, ACS712 current sensor, a voltage sensor in the form of a voltage divider and a converter circuit with a SEPIC topology controlled by an Arduino UNO microcontroller with an MPPT sistem.The results obtained as follows: a good placement of solar panels is facing upward (perpendicular to the surface of the earth, current sensors work with an average eror of 1.92%, voltage sensors have an average eror of 2.76%, and SEPIC topology with MPPT has an average power yield of 26.13 W.

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.

Sign in / Sign up

Export Citation Format

Share Document