TY - GEN
T1 - PV Array Fault Detection using Radial Basis Networks
AU - Pedersen, Emma
AU - Rao, Sunil
AU - Katoch, Sameeksha
AU - Jaskie, Kristen
AU - Spanias, Andreas
AU - Tepedelenlioglu, Cihan
AU - Kyriakides, Elias
N1 - Funding Information:
ACKNOWLEDGEMENTS This study was supported in part by the NSF IRES program award 1854273 and the NSF CPS award 1659871. Logistical support was provided by the ASU SenSIP center.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - An increase in grid-connected photovoltaic arrays creates a need for efficient and reliable fault detection. In this paper, machine learning strategies for fault detection are presented. An Artificial Neural Network was studied with the goal of detecting three photovoltaic module conditions. In addition, an unsupervised approach was successfully implemented using the-means clustering algorithm, successfully detecting arc and ground faults. To distinguish and localize additional faults such as shading and soiling, a supervised approach is adopted using a Radial Basis Function Network. A solar array dataset with voltage, current, temperature, and irradiance was examined. This dataset had labeled data with normal conditions and faults due to soiling and shading. A radial basis network was trained to classify faults, resulting in an error rate below 2% on synthetic data with realistic levels of noise.
AB - An increase in grid-connected photovoltaic arrays creates a need for efficient and reliable fault detection. In this paper, machine learning strategies for fault detection are presented. An Artificial Neural Network was studied with the goal of detecting three photovoltaic module conditions. In addition, an unsupervised approach was successfully implemented using the-means clustering algorithm, successfully detecting arc and ground faults. To distinguish and localize additional faults such as shading and soiling, a supervised approach is adopted using a Radial Basis Function Network. A solar array dataset with voltage, current, temperature, and irradiance was examined. This dataset had labeled data with normal conditions and faults due to soiling and shading. A radial basis network was trained to classify faults, resulting in an error rate below 2% on synthetic data with realistic levels of noise.
KW - Fault detection
KW - Machine learning
KW - Radial basis networks
KW - Solar energy
UR - http://www.scopus.com/inward/record.url?scp=85075887860&partnerID=8YFLogxK
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U2 - 10.1109/IISA.2019.8900710
DO - 10.1109/IISA.2019.8900710
M3 - Conference contribution
AN - SCOPUS:85075887860
T3 - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
BT - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019
Y2 - 15 July 2019 through 17 July 2019
ER -