启发式热电发电多峰值MPPT方法研究  

Heuristic Multi-Peak MPPT Method for Thermoelectric Power Generation

作　　者：郭轩成 陈逸峰 朱文超 谭保华[1] GUO Xuan-cheng;CHEN Yi-feng;ZHU Wen-chao;TAN Bao-hua(School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan Hubei 430068,China;School of Automation,Wuhan University of Technology,Wuhan Hubei 430070,China;Hubei Key Laboratory of Modern Auto Parts Technology,Wuhan Hubei 430070,China)

机构地区：[1]湖北工业大学电气与电子工程学院,湖北武汉430068 [2]武汉理工大学自动化学院,湖北武汉430070 [3]现代汽车零部件技术湖北省重点实验室,湖北武汉430070

出　　处：《计算机仿真》2022年第12期84-92,共9页Computer Simulation

基　　金：国家自然科学基金面上项目(51977164)。

摘　　要：温度的不均匀分布导致热电发电机(TEG)的功率特性曲线呈现多峰值,传统最大功率追踪(MPPT)方法由于其固定的步长以及随机性的缺乏,导致无法跟踪到全局最大功率点(GMPP)。为解决上述问题,提出了一种新的启发式算法即全局飞鼠搜索-粒子群混合算法(GFSS-PSO),用于热电发电机的最大功率点跟踪。GFSS-PSO在原有粒子群算法中引入了跳跃机制和捕食者机制,以避免粒子陷入局部最优并减小追踪过程的震荡。最后,与扰动观察法,标准粒子群算法,改进粒子群算法进行仿真对比,结果表明所提出的算法在用于MPPT时可以更加准确并快速地跟踪GMPP。The uneven distribution of temperature causes the power characteristic curve of the thermoelectric generator(TEG) to show multiple peaks, and the traditional maximum power point tracking(MPPT) method is unable to track the global maximum power point(GMPP) due to its fixed step size and lack of randomness. A new heuristic algorithm for maximum power point tracking of thermoelectric generators is proposed in this research, called the global flying squirrel search-particle swarm optimization(GFSS-PSO) hybrid algorithm. The algorithm integrates the jumping mechanism and the predator mechanism of the squirrel search algorithm into the original particle swarm algorithm to prevent the particles from falling into the local optimum and reduce the oscillation of the tracking process. Finally, the algorithm is compared with the perturbation observation method, standard particle swarm algorithm, and improved particle swarm algorithm. Simulations indicate that the proposed algorithm can track the GMPP more accurately and quickly when used for MPPT.

关 键 词：热电发电机 最大功率点跟踪 粒子群算法 捕食者机制 

分 类 号：TM743[电气工程—电力系统及自动化]