Tilt Angle Optimization for Grid Interactive Solar Photovoltaic Array

2011 ◽  
Vol 110-116 ◽  
pp. 4554-4558
Author(s):  
Ranchan Chauhan ◽  
N.S. Thakur ◽  
Sunil Chamoli

The overall performance of any solar energy project largely depends upon the available solar radiations, inclination and orientation of solar collectors. Presented in this paper is the analytical study on optimum tilt angles and lifetime differential savings for a distributed 200 kW grid connected mono-crystalline solar PV system operating at Khatkar Kalan, Punjab, India. The optimum tilt angles for monthly, seasonally and yearly basis is carried out by searching the values of tilt angle for which electric power output is maximum for a particular day or a specific period using energy conversion model. The results reveal that the yearly optimum tilt angle for the SPV plant at Khatkar Kalan is 36° which is 4.58° higher than the latitude angle. The power output from the array increases with increase in angle of tilt for winter months whereas the trend is reverse for the summer months. In winter months the maximum power output is achieved for the array surface with a tilt of angle 13° - 23° higher than the local latitude while for summer months the maximum power output is achieved at 16° lower than the latitude angle. The optimum tilt angles maximizing monthly power output for south facing surface shows that the monthly optimum tilt angle varies from 15° to 55°. Also the parametric analysis for some influential factors such as latitude of location and reflectivity of ground surface is explored.

Author(s):  
C. Pavithra ◽  
Pooja Singh ◽  
Venkatesa Prabhu Sundramurthy ◽  
T.S. Karthik ◽  
P.R. Karthikeyan ◽  
...  

2019 ◽  
Vol 5 (11) ◽  
pp. 1-9
Author(s):  
Avinash Kumar ◽  
Vivek Kumar Kostha ◽  
Satyam Kumar Prasun

This work deals with neural network control algorithm-based grid connected to solar photo voltaic (PV) system consisting of DC-AC converter. The reference solar-grid current for three-leg VSC are estimated using neural network control algorithm. The neural network control algorithm based solar PV system is modeled in MATLAB R2018a along with SIMULINK.. This study presents an artificial neural network-based controller for regulating the level of active and reactive power output. First, the three phase currents from the VSI are measured and compared with the three reference currents. The neural network is trained to have minimum output error. It was concluded that the power output from the system was found to be190 KVA in case of system having no intelligent controller and 700 KVA in case of system with AAN based control. The voltage of output is maintained to be 20 kV in the grid system for analysis purpose. Thus the proposed control is expected to be implemented in the renewable energy resources for better output.


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