Remote Monitoring and Fault Diagnosis of Ocean Current Energy Hydraulic Transmission and Control Power Generation System

Wenbin Su, Hongbo Wei, Penghua Guo and Ruizhe Guo
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Wenbin Su: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Hongbo Wei: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Penghua Guo: School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Ruizhe Guo: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2021, vol. 14, issue 13, 1-18

Abstract: The development of clean and environmentally friendly energy is necessary to address significant energy challenges, and abundant sea current energy, which plays a key role in the decarbonization of our energy systems and has attracted increasing attention among researchers. In the present study, a remote monitoring and diagnosis system was designed in accordance with the requirements of a 50 kW hydraulic transmission and control power generation system. Hardware selection and software function requirement analysis were then performed. The causes of system faults were analyzed, the output fault types of the improved model were determined, and effective monitoring parameters were selected. The accuracy of traditional spectra in diagnosing faults is poor; however, the generalization capability of support vector machines (SVM) is robust. Thus, an improved particle swarm algorithm optimized SVM fault diagnosis model for the hydraulic transmission control power generation system was proposed to rapidly and effectively determine the key parameters. Remote monitoring software for the hydraulic transmission and control power generation system was also developed. The results of remote monitoring and diagnostic tests showed that the software was able to satisfy the functional requirements of the hydraulic transmission control power generation remote monitoring system, and the operation effect was consistent with expectations. By comparing the test accuracy of different diagnostic models, the improved PSVM model has the highest test accuracy with a classification accuracy of 99.4% in the case of normal operation, accumulator failure, relief valve failure and motor failure. In addition, the proposed diagnostic method was effective, thereby ensuring safe and reliable operation of the hydraulic transmission control power generation system.

Keywords: ocean current energy; hydraulic transmission; power generation; remote monitoring and fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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