Open AccessIoT‐based approach to condition monitoring of the wave power generation system
Author(s) -
Qian Peng,
Feng Bo,
Zhang Dahai,
Tian Xiange,
Si Yulin
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2018.5918
Subject(s) - computer science , reliability (semiconductor) , upload , cloud computing , fault (geology) , real time computing , electric power system , condition monitoring , power (physics) , key (lock) , extreme learning machine , reliability engineering , engineering , electrical engineering , artificial intelligence , artificial neural network , physics , computer security , quantum mechanics , seismology , geology , operating system
Accurate and reliable fault detecting plays a key role in application of grid‐connected wave power generation systems. This study presents a novel IoT‐based approach to condition monitoring of the wave power generation system, which has faster operating rate and lower hardware requirement. The compressed sensing (CS) method is adopted to compress the data, which aims to reduce the data uploaded to cloud platform; and then, the extreme learning machine (ELM) algorithm is used to achieve the condition monitoring of wave power generation system in cloud platform. In order to validate the effectiveness of the proposed method, the IoT‐based wave power generation condition monitoring system test platform is established. The experiment results illustrate the high efficiency and reliability of proposed method. The proposed method has a potential of practical applications.