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