TY - GEN
T1 - Photovoltaic array simulation and fault prediction via multilayer perceptron models
AU - Khondoker, Farib
AU - Rao, Sunil
AU - Spanias, Andreas
AU - Tepedelenlioglu, Cihan
N1 - Funding Information:
ACKNOWLEDGEMENTS This material is based upon work supported by the National Science Foundation GOALI Award 1646542, a GOALI award REU supplement, the NSF CPS program award number 1659871, along with the financial and logistical support of the ASU SenSIP center.
Funding Information:
This material is based upon work supported by the National Science Foundation GOALI Award 1646542, a GOALI award REU supplement, the NSF CPS program award number 1659871, along with the financial and logistical support of the ASU SenSIP center.
Publisher Copyright:
© 2018 IEEE
PY - 2019/2/1
Y1 - 2019/2/1
N2 - When collecting solar energy via photovoltaic (PV) panel arrays, one common issue is the potential occurrence of faults. Faults arise from panel short-circuit, soiling, shading, ground leakage and other sources. Machine learning algorithms have enabled data-based classification of faults. In this paper, we present an Internet-based PV array fault monitoring simulation using the Java-DSP (J-DSP) simulation environment. We first develop a solar array simulation in J-DSP and then form appropriate graphics to examine V-I curves, maximum power point tracking, and faults. We then introduce a multi-layer perceptron model for PV fault detection. We deploy and assess the simulation by disseminating to a group of users that provide feedback.
AB - When collecting solar energy via photovoltaic (PV) panel arrays, one common issue is the potential occurrence of faults. Faults arise from panel short-circuit, soiling, shading, ground leakage and other sources. Machine learning algorithms have enabled data-based classification of faults. In this paper, we present an Internet-based PV array fault monitoring simulation using the Java-DSP (J-DSP) simulation environment. We first develop a solar array simulation in J-DSP and then form appropriate graphics to examine V-I curves, maximum power point tracking, and faults. We then introduce a multi-layer perceptron model for PV fault detection. We deploy and assess the simulation by disseminating to a group of users that provide feedback.
UR - http://www.scopus.com/inward/record.url?scp=85062843955&partnerID=8YFLogxK
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U2 - 10.1109/IISA.2018.8633699
DO - 10.1109/IISA.2018.8633699
M3 - Conference contribution
AN - SCOPUS:85062843955
T3 - 2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018
BT - 2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018
Y2 - 23 July 2018 through 25 July 2018
ER -