IoT‐based approach to condition monitoring of the wave power generation system

Abstract

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.