机电工程技术2025，Vol.54Issue(9)：78-83,6.DOI:10.3969/j.issn.1009-9492.2024.00153
基于粒子群优化算法的光伏发电系统MPPT控制策略及仿真研究
Study and Simulation of Photovoltaic System MPPT Control Strategy Based on Particle Swarm Optimization Algorithm
摘要
Abstract
Aiming to solve the problem of failure of traditional maximum power point tracking(MPPT)control algorithm for solar photovoltaic(PV)power generation system under partial shading conditions,and to ensure that the system can be operated efficiently under complex lighting environments.By introducing the particle swarm optimization(PSO)algorithm,the duty cycle and its perturbation increments are used as the particle positions and velocities,and the fitness function is constructed to evaluate the particle performance with the objective of maximizing the PV output power.The algorithmic process includes calculating the individual optimal pbest(i)and the global optimal gbest,and iteratively updating the particle state based on the PSO rules to realize the dynamic tracking of the maximum power point.A simulation model of off-grid PV power generation system is constructed based on MATLAB/Simulink to verify the effectiveness of the proposed algorithm.The results show that the algorithm can quickly converge to the optimal solution,significantly improve the power generation efficiency of the system under different light conditions,showing stronger adaptability and robustness compared with traditional methods,and the algorithm has a simple structure,which is easy to be realized on microcontrollers such as microcontrollers or DSP,and it provides an effective solution to improve the level of intelligence of the photovoltaic system.关键词
光伏系统/最大功率点跟踪/粒子群优化算法/MATLAB/SimulinkKey words
photovoltaic system/maximum power point tracking/particle swarm optimization algorithm/MATLAB/Simulink分类
信息技术与安全科学引用本文复制引用
彭吉康,王怀平,马善农,夏洪,马玥明,颜吉骏..基于粒子群优化算法的光伏发电系统MPPT控制策略及仿真研究[J].机电工程技术,2025,54(9):78-83,6.基金项目
国家自然科学基金(61463001) （61463001）
江西省教育厅科学技术研究项目(GJJ190374) （GJJ190374）
江西省新能源工艺及装备工程技术研究中心开放基金项目(JXNE2019-07) （JXNE2019-07）
