A new offline mobile device for extracting I-V characteristic curve for photovoltaic modules
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Date
2021
Authors
Nabulsi, Lina
Khushashi, Heba
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Abstract
This project introduces an improved device using hybrid learning machine
system for I-V curve prediction. Two cascaded forward neural network were
the basis for the proposed device. Factor X is the predicted output from the
first cascaded neural network which was used as input for the second cascaded
neural network in order to eliminate correlation between variables. The cas caded neural network was used to predict the actual current for a solar cell.
In the learning process of the proposed device, an experimental dataset that
involves records of ambient temperature, solar radiation, voltage and current
for different photovoltaic modules is used. The device built to be general for
all solar cell modules where the device input’s are ambient temperature, solar
radiation, and datasheet specifications of photovoltaic module (short circuit
current and open circuit voltage). Matlab is used to train, test and validate
the proposed model, then the weights were used in equations in the python
program, in order to download it on the Raspberry Pi to propose an easy
and general device with a friendly GUI. The proposed device is very helpful
and useful in predicting I-V curves for various photovoltaic modules.