DocumentCode :
1517167
Title :
Neural Network Estimation of Microgrid Maximum Solar Power
Author :
Chatterjee, Abir ; Keyhani, Ali
Author_Institution :
Ohio State Univ., Columbus, OH, USA
Volume :
3
Issue :
4
fYear :
2012
Firstpage :
1860
Lastpage :
1866
Abstract :
The integration of photovoltaic (PV) generating stations in the power grids requires the amount of power available from the PV to be estimated for power systems planning on yearly basis and operation control on daily basis. To determine the PV station maximum output power, the PV panels must be placed at an optimal tilt angle to absorb maximum energy from the sun. This optimal tilt angle is a nonlinear function of the location, time of year, ground reflectivity and the clearness index of the atmosphere. This paper proposes a neural network (NN) to estimate the optimal tilt angle at a given location and thus an estimate of the amount of energy available from the PV in a microgrid.
Keywords :
distributed power generation; neural nets; nonlinear functions; photovoltaic power systems; power engineering computing; power generation planning; power grids; NN; PV generating stations; PV panels; ground reflectivity; microgrid maximum solar power; neural network estimation; nonlinear function; optimal tilt angle; photovoltaic integration; power grids; power systems planning; sun; Artificial neural networks; Estimation; Photovoltaic systems; Power generation; Radiation effects; Terrestrial atmosphere; Irradiation; neural network; photovoltaic systems; power estimation; tilt angle;
fLanguage :
English
Journal_Title :
Smart Grid, IEEE Transactions on
Publisher :
ieee
ISSN :
1949-3053
Type :
jour
DOI :
10.1109/TSG.2012.2198674
Filename :
6200398
Link To Document :
بازگشت