Solar Array Fault Detection using Neural Networks

被引:0
作者
Rao, Sunil [1 ]
Spanias, Andreas [1 ]
Tcpedelenlioglu, Cihan [1 ]
机构
[1] Arizona State Univ, Sch ECEE, SenSIP Ctr, Tempe, AZ 85287 USA
来源
2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER PHYSICAL SYSTEMS (ICPS 2019) | 2019年
关键词
Cyber-Physical Systems; Photovoltaics (PV); Fault Detection; Solar Energy; Machine Learning; Neural Networks; OPTIMIZATION; SIMULATION;
D O I
10.1109/icphys.2019.8780208
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:196 / 200
页数:5
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