局部阴影条件下,光伏发电系统中P-U曲线会呈现多峰现象,传统的最大功率点跟踪(Maximum Power Point Tracking, MPPT)算法易失效,粒子群(PSO)算法适用于复杂多极值系统的寻优,因而在多峰全局MPPT中得到应用。针对寻优过程中传统PSO算法搜索精度低以及易出现早熟现象的缺点,本文提出了自适应惯性权重粒子群(APSO)算法,在PSO算法中引入非线性惯性权重,以提高多峰全局寻优的精度与速度。最后利用MATLAB/Simulink对系统进行仿真,仿真结果表明：在均匀光照和可变阴影条件下,APSO算法能有效提高系统寻优的收敛速度与精度。
英文摘要:
Under the condition of local shadow, the P-U curve in photovoltaic power generation system will show multi-peak phenomenon, and the traditional maximum power point tracking (Maximum Power Point Tracking, MPPT) algorithm is easy to fail. Particle swarm optimization (PSO) algorithm is suitable for the optimization of complex multi-extremum systems, so it is applied in multi-peak global MPPT. Aiming at the shortcomings of low search precision and premature phenomenon of traditional PSO algorithm in the optimization process, the self-adaptive inertial weight particle swarm (APSO) algorithm is proposed. Nonlinear inertia weight is introduced into PSO algorithm to improve the accuracy and speed of multi-peak global optimization. Finally, the system is simulated by MATLAB/Simulink. The simulation results show that the APSO algorithm can effectively improve the convergence speed and accuracy of the system under the condition of uniform light and variable shadow.
