@inproceedings{0371ff88df164dfa8188dc23462b7806,
title = "Solar array fault detection using neural networks",
abstract = "In this paper, we describe a Cyber-Physical system approach to fault detection in Photovoltaic (PV) arrays. More specifically, we explore customized neural network algorithms for fault detection from monitoring devices that sense data and actuate at each individual panel. We develop a framework for the use of feedforward neural networks for fault detection and identification. Our approach promises to improve efficiency by detecting and identifying eight different faults and commonly occurring conditions that affect power output in utility scale PV arrays.",
keywords = "Cyber-Physical Systems, Fault Detection, Machine Learning, Neural Networks, Photovoltaics (PV), Solar Energy",
author = "Sunil Rao and Andreas Spanias and Cihan Tepedelenlioglu",
note = "Funding Information: ACKNOWLEDGEMENT This research is supported in part by the NSF CPS Award number 1646542 and by the ASU SenSIP center. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019 ; Conference date: 06-05-2019 Through 09-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICPHYS.2019.8780208",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "196--200",
booktitle = "Proceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019",
}