Abstract:
A solar photovoltaic (PV) system is currently gaining prominence as a cost-effective
renewable energy source, making a significant contribution to the energy sector. Its
environmentally friendly nature further enhances its appeal. The main challenge of the PV
system is it depends upon the solar irradiation and any changes in the incoming solar
irradiation will affect badly on the output of the PV system. However, the ongoing effort to
improve rural electrification by enhancing the performance of solar systems continues in
order to effectively utilize the better maximum power point tracking (MPPT) controller. The
Zeta converter, due to its low
1
input
and
output
current
ripple, low electromagnetic
interference (EMI), better reliability, input-output isolation ability and better
variety
adaptability
in
a
of applications, is used for this study. This work consists modeling of solar panels,
charge controllers, battery system,
5
and
Artificial
Neural
Network
(ANN) based MPPT
controller. The proposed approach was considered to design the PV based power
generation system for Arba Minch Zuria woreda Zigit-Bakole health center and Zigit
Merche school, Gamo Zone, Ethiopia. The irradiation of the sun is subjected to vary as
time progress in a day and the required irradiance data was collected for the proposed site.
As the collected data, reveals the proposed site has peak 6-8 hours sunshine with the
maximum demand of about 1197W and 1352W for both sites respectively. Lead acid
battery is considered to supply the load during night time. The ANN-based MPPT algorithm
is designed to change the duty cylce of the converter to maximize PV output. The PI
controller based inverter is modeled for supplying the AC loads in both sites.
results
of
the
5
The
proposed work have been compared with and without ANN and Pertrub and
observe (P&O) based MPPT controller for Zeta converter. The better performance ANN
based MPPT
1
implementation.
of
the
Zeta
converter
is suggested for prototype development and
