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| Maximum Power Point Tracking of Centralized Thermoelectric Generation System using Greedy Neural Network |
| Yang Bo1, Wang Junting1, Zhong Linen1, Shu Hongchun1, Yu Tao2, Zhang Xiaoshun3, Tan Tian3 |
1. Faculty of Electric Power Engineering Kunming University of Science and Technology Kunming 650500 China;
2. College of Electric Power South China University of Technology Guangzhou 510640 China;
3. College of Engineering Shantou University Shantou 515063 China |
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Abstract A novel greedy search based neural network (GSNN) for centralized thermoelectric generation (TEG) system under non-uniform temperature distribution (NTD) condition is designed to achieve maximum power point tracking (MPPT) in this paper. Firstly, a two-layer feed-forward neural network model is established, where input is defined as duty cycle of DC-DC boost converter while output as power output of TEG system. Then, Levenberg-Marquardt method is adopted to train neural network, accordingly the I/O curve that draws an evident distinction between local maximum power point (LMPP) and global maximum power point (GMPP) is fitted. Meanwhile, a greedy search is well performed to explore GMPP over a compressed search range. Lastly, three case studies are carried out, i.e., constant temperature, step temperature and sensitivity analysis. Simulation results demonstrate that GSNN could achieve maximum power output with speediness and stability under NTD compared to perturb and observe (P&O), particle swarm optimization (PSO) and grey wolf optimizer (GWO). Furthermore, a dSpace based hardware-in-the-loop (HIL) experiment is undertaken to validate implementation feasibility of the proposed algorithm.
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Received: 17 December 2019
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2020, Vol. 35 
