scholarly journals Survey on Photo-Voltaic Powered Interleaved Converter System

2020 ◽  
Author(s):  
M.L. Bharathi
Keyword(s):  
Photo Voltaic

Smart Solid State Circuit Breaker for Photo Voltaic Power Plants

2017 ◽  
Vol 12 (5) ◽  
pp. 409 ◽  
Author(s):  
Luigi Rubino ◽  
Guido Rubino ◽  
Pompeo Marino ◽  
Luigi Pio Di Noia
Keyword(s):  
Solid State ◽  
Power Plants ◽  
Circuit Breaker ◽  
Solid State Circuit ◽  
Photo Voltaic ◽  
Solid State Circuit Breaker

Fuzzy Logic Based MPPT Scheme for SEPIC Converter in Photo- Voltaic System

2016 ◽  
Vol 4 (1) ◽  
pp. 23
Author(s):  
SINGH SHWETA ◽  
KUMAR AWADHESH ◽  
◽  
Keyword(s):  
Fuzzy Logic ◽  
Photo Voltaic

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

Experimental investigation of dust deposition effects on photo-voltaic output performance

Solar Energy ◽  
2018 ◽  
Vol 159 ◽  
pp. 346-352 ◽  
Author(s):  
Aslan Gholami ◽  
Iman Khazaee ◽  
Shahab Eslami ◽  
Majid Zandi ◽  
Ehsan Akrami
Keyword(s):  
Experimental Investigation ◽  
Dust Deposition ◽  
Output Performance ◽  
Photo Voltaic

Photo-voltaic Properties of Metal-Oxide-CdS Solar Cells

1978 ◽  
Vol 24 (6) ◽  
pp. 253-256 ◽  
Author(s):  
Krishna P. Pande ◽  
Balbir Singh
Keyword(s):  
Solar Cells ◽  
Metal Oxide ◽  
Photo Voltaic
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