On-line monitoring system of PV array based on internet of things technology

2017 International Conference on New Energy and Future Energy System

Li, Y.F.^1,2 ; Lin, P.J.^1,2 ; Zhou, H.F.^1,2 ; Chen, Z.C.^1,2 ; Wu, L.J.^1,2 ; Cheng, S.Y.^1,2 ; Su, F.P.^1,2

Institute of Micro/Nano Devices and Solar Cells, College of Physics and Information Engineering, Fuzhou University, Fuzhou

350116, China^1

Jiangsu Collaborative Innovation Centre of Photovoltaic Science and Engineering, Changzhou

213164, China^2

关键词: Environmental parameter;    Extreme learning machine;    Geographic information;    Internet of thing (IOT);    Internet of things technologies;    On-line monitoring system;    Operating condition;    Operating environment;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/93/1/012078/pdf
DOI  :  10.1088/1755-1315/93/1/012078

来源: IOP

【 摘 要 】

The Internet of Things (IoT) Technology is used to inspect photovoltaic (PV) array which can greatly improve the monitoring, performance and maintenance of the PV array. In order to efficiently realize the remote monitoring of PV operating environment, an on-line monitoring system of PV array based on IoT is designed in this paper. The system includes data acquisition, data gateway and PV monitoring centre (PVMC) website. Firstly, the DSP-TMS320F28335 is applied to collect indicators of PV array using sensors, then the data are transmitted to data gateway through ZigBee network. Secondly, the data gateway receives the data from data acquisition part, obtains geographic information via GPS module, and captures the scenes around PV array via USB camera, then uploads them to PVMC website. Finally, the PVMC website based on Laravel framework receives all data from data gateway and displays them with abundant charts. Moreover, a fault diagnosis approach for PV array based on Extreme Learning Machine (ELM) is applied in PVMC. Once fault occurs, a user alert can be sent via E-mail. The designed system enables users to browse the operating conditions of PV array on PVMC website, including electrical, environmental parameters and video. Experimental results show that the presented monitoring system can efficiently real-time monitor the PV array, and the fault diagnosis approach reaches a high accuracy of 97.5%.