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Maximum efficiency point tracking for an ocean thermal energy harvesting system
Author(s) -
Xia Qingchao,
Chen Yanhu,
Yang Canjun,
Chen Bingzhe,
Muhammad Gul,
Ma Xiaoxia
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5055
Subject(s) - particle swarm optimization , efficient energy use , energy transformation , energy conversion efficiency , marine energy , control theory (sociology) , electricity generation , pid controller , computer science , ocean thermal energy conversion , maximum power principle , thermal energy , economic shortage , energy (signal processing) , power (physics) , environmental science , photovoltaic system , engineering , solar energy , mathematics , control engineering , temperature control , algorithm , control (management) , electrical engineering , physics , artificial intelligence , statistics , philosophy , government (linguistics) , linguistics , quantum mechanics , thermodynamics
Summary Limited energy is the most critical factor that restricts the persistent presence of underwater vehicles in the oceans; thus, harvesting the ocean's thermal energy that is stored in the water column between the sea surface and deep water is a particularly promising solution for the current power shortage. This paper has designed a new ocean thermal energy conversion system which using phase change material as energy storage medium, and proposed a novel maximum efficiency point tracking (MEPT) method for energy conversion. This new method, which is integrated with a radial basis function neural network (RBFNN), particle swarm optimization (PSO) and the proportion integration differentiation (PID) control method, could effectively improve the efficiency of energy conversion. Compared with the power generation system that does not use the MEPT method, experimental results show that the proposed method can improve the efficiency of the power generation from less than 19.05% to more than 34.3% and has higher stability (using this method: the efficiency changes from 34.3%‐34.7%; without using this method: the efficiency changes from 13.56% ‐19.05%) when the load changes. This novel method can be used in many conditions, especially when the mathematical model of the generation system is unknown or researchers want to use fewer sensors for maximum efficiency point tracking.