Open AccessParameters estimation of hydraulic power take-off system for wave energy conversion system using genetic algorithm
Author(s) -
Mohd Afifi Jusoh,
Mohd Zamri Ibrahim,
Muhamad Zalani Daud,
Zulkifli Mohd Yusop,
Aliashim Albani,
Sejuti Rahman,
Safina Mohad
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/463/1/012129
Subject(s) - hydraulic machinery , accumulator (cryptography) , hydraulic motor , genetic algorithm , control theory (sociology) , engineering , process (computing) , power (physics) , hydraulic cylinder , displacement (psychology) , reduction (mathematics) , computer science , algorithm , mechanical engineering , mathematics , psychology , physics , geometry , control (management) , quantum mechanics , machine learning , artificial intelligence , psychotherapist , operating system
This paper presents accurate control parameters estimation of the hydraulic Power Take-Off (PTO) model for the wave energy conversion system to maximise energy production. In general, the performance of the hydraulic PTO system depends on the parameters setting of hydraulic PTO system components such as hydraulic motor displacement setting, pre-charge of the hydraulic accumulator, and et cetera. Conventionally, it requires to manually obtain the optimal parameters of a hydraulic PTO system by repeating the simulation process. However, this estimation method exposed to human error and would easily be resulting in a non-optimal selection of hydraulic PTO parameters for the wave energy conversion system. Therefore, an easy and accurate approach of using the GA optimisation method for determining hydraulic PTO parameters was introduced in the present study. This approach is simple and more accurate compared to the conventional optimisation method. The hydraulic PTO model was developed in SIEMENS/Amesim environment using available components in the library. The specifications of the actual hydraulic PTO system components from the manufacturer were used during the simulation set-up. The complete hydraulic PTO system was optimised using a special genetic algorithm (GA) optimisation tools in the SIEMENS/Amesim software. The simulation results showed that GA was effective to determine the optimal configuration parameters of hydraulic PTO system. From the results, the optimal configuration parameters of hydraulic PTO system were successfully reduced about 38%. Consequently, the maximum force applied to the WEC devices was reduced up to 34%. This force reduction is important since it will enable the WECS to be operated during a smaller wave condition.