基于自适应罗盘搜索的集中式温差发电系统MPPT设计

提出了一种新型自适应罗盘搜索(Adaptive Compass Search, ACS)算法，用于非均匀温差分布(Non-uniform Temperature Distribution, NTD)条件下的集中式温差发电(Thermoelectric Generation, TEG)系统最大功率点跟踪(Maximum Power Point Tracking, MPPT)。集中式TEG系统仅使用一个MPPT变换器，因此与组串式和模块式的TEG系统结构相比，其运行与维护成本较低。然而，在NTD条件下，集中式TEG系统通常会出现多个最大功率点(Maximum Power Point, MPP)。为有效寻找集中式TEG系统的全局MPP，采用了一种基于探索方向的自适应序列方式，通过利用过去的搜索结果来显著提高ACS的全局搜索能力。通过三个算例对ACS的MPPT性能进行了研究，即温度恒定、温度阶跃变化以及随机温度变化。仿真结果表明，与扰动观测(Perturb and Observe, P&O)算法、粒子群优化(Particle Swarm Optimization, PSO)算法和罗盘搜索(Compass Search, CS)算法相比，ACS能以更快的速度和更高的收敛稳定性获得高质量的全局MPP。

MPPT design of centralized thermoelectric generation system using adaptive compass search

This paper proposes a novel Adaptive Compass Search (ACS) for Maximum Power Point Tracking (MPPT) of a centralized Thermoelectric Generation (TEG) system under the condition of Non-uniform Temperature Distribution (NTD). The centralized TEG system employs only one MPPT converter and thus the overall costs of operation and maintenance can be significantly reduced compared to that of string-type and modularized configurations. However, multiple Maximum Power Points (MPPs) usually appear in a centralized TEG system under an NTD condition. In order to effectively and efficiently seek the global MPP, an adaptive sequence of exploration directions is employed to considerably enhance the global searching ability of ACS through the previous searching results. Three case studies are carried out to study the MPPT performance of ACS, including constant temperature difference, step change of temperature, and random temperature variation. Simulation results show that ACS can obtain a high-quality global MPP with a faster speed and a higher convergence stability than those of Perturb and Observe (P&O), Particle Swarm Optimization (PSO), and Compass Search (CS), respectively. This work is supported by National Natural Science Foundation of China (No. 61963020).