ABSTRACT:
This paper propounds a novel technique for detection
of the faults in photovoltaic array by using Artificial Neural Network. By
using a simulation model, the power variation under different faulty conditions
such as open circuit fault, short circuit fault, and bridging fault are
measured under normal and partial shading conditions. The simulated attributes are
given to the Artificial Neural Network to predict the type of fault occurred in
or between photovoltaic modules. Finally, three different training algorithms
of Artificial Neural Network are compared for fault detection with help of mean
square error as the performance parameter.
KEYWORDS:
1.
Fault detection
2.
Photovoltaic array
3.
Artificial neural network
4.
Open circuit fault
5.
Short circuit fault
6.
Bridging fault
SOFTWARE:
MATLAB/SIMULINK
CONCLUSION:
In
this paper, the fault detection method is proposed to detect the fault occurred
in or between the PV modules. The performance of PV module has been
investigated in this paper for different faults such as open circuit fault,
short circuit fault and bridging fault under normal and partial shaded conditions.
ANN is used to detect the faults occurred in or between PV modules under normal
and partial shading conditions and it is tested with random inputs. Future work
is to locating the fault in the large scale PV system.
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[1]
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[2]
Kashif Ishaque and Zainal Salam, “ A review of maximum power point tracking
techniques of PV system for uniform insolation and partial shading condition”,
International Journal on Renewable and Sustainable Energy Reviews, Vol.19,
pp.475-488,2013.
[3]
M. Sabbaghpur Arani and M.A Hejazi, “ The comprehensive study of electrical
faults in PV array”, Journal of Electrical and Computer Engineering, Volume
2016.
[4]
S.Saravan and N.Ramesh Babu, “Maximum power point tracking algorithms for the
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Sustainable Energy Reviews, Vol.57, pp.192-204, 2016.
[5]
K.L.Lian, J.H.Jhang, and I.S.Tian, “ A maximum power point tracking method
based on perturb-and-observe combined with particle swarm optimization”, IEEE
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