globalchange  > 气候变化与战略
DOI: 10.1016/j.apenergy.2019.114232
论文题名:
Greedy search based data-driven algorithm of centralized thermoelectric generation system under non-uniform temperature distribution
作者: Zhang X.; Tan T.; Yang B.; Wang J.; Li S.; He T.; Yang L.; Yu T.; Sun L.
刊名: Applied Energy
ISSN: 3062619
出版年: 2020
卷: 260
语种: 英语
英文关键词: Centralized thermoelectric generation system ; Data-driven ; Greedy search ; MPPT ; Neural network ; Non-uniform temperature distribution
Scopus关键词: Feedforward neural networks ; Global optimization ; Maximum power point trackers ; Multilayer neural networks ; Neural networks ; Particle swarm optimization (PSO) ; Sensitivity analysis ; Stochastic systems ; Temperature distribution ; Approximation curves ; Data driven ; Data-driven algorithm ; Greedy search ; Hard-ware-in-the-loop ; Maximum Power Point Tracking ; Optimization algorithms ; Thermoelectric generation systems ; Search engines ; artificial neural network ; energy efficiency ; power generation ; temperature gradient ; thermal power ; Cetacea
英文摘要: The generation efficiency of thermoelectric generation system is relatively low, thus how maximize its power production is of great importance. This paper designs a novel greedy search based data-driven method for centralized thermoelectric generation system to achieve maximum power point tracking under non-uniform temperature distribution. In order to effectively distinguish the local maximum power points and the global maximum power point under non-uniform temperature distribution, greedy search based data-driven employs a two-layer feed-forward neural network to accurately fit the curve between the power output and the controllable variable based on the real-time updated operation data. Based on the approximation curve, a greedy search is designed to efficiently approach the global maximum power point from a shrinking search space. Cases studies such as start-up test, step variation of temperature, stochastic temperature change, and analyse of sensitivity, are implemented to prove the effectiveness and superiority of the proposed algorithm. Simulation results verify that the proposed method can generate the highest energy under non-uniform temperature distribution condition, e.g., 391.34%, 115.71%, 110.92%, and 109.43% to that of perturb and observe, particle swarm optimization, whale optimization algorithm, and grey wolf optimizer in the stochastic temperature change. Lastly, the implementation feasibility of the proposed method is demonstrated by the hardware-in-the-loop experiment based on dSpace platform. © 2019 Elsevier Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159278
Appears in Collections:气候变化与战略

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作者单位: College of Engineering, Shantou University, Shantou, 515063, China; Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming, 650217, China; Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China; College of Electric Power, South China University of Technology, Guangzhou, 510640, China; Guangzhou Shuimuqinghua Technology Co. Ltd., Guangzhou, 510898, China; Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, Shantou, 515063, China; Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou, 515063, China

Recommended Citation:
Zhang X.,Tan T.,Yang B.,et al. Greedy search based data-driven algorithm of centralized thermoelectric generation system under non-uniform temperature distribution[J]. Applied Energy,2020-01-01,260
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