Greedy search based data-driven algorithm of centralized thermoelectric generation system under non-uniform temperature distribution. (15th February 2020) - Zhang, Xiaoshun; Tan, Tian; Yang, Bo; Wang, Jingbo; Li, Shengnan; He, Tingyi; Yang, Lei; Yu, Tao; Sun, Liming
Graphical abstract: Highlights: A maximum power point tracking of thermoelectric generation system is constructed. The power output feature is analyzed under non-uniform temperature distribution. A greedy search based data-driven method is used for maximum power point tracking. The proposed method can rapidly search a high-quality maximum power point. Both of energy loss and power fluctuation can be reduced by the proposed method. Abstract: 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.