基于二阶振荡粒子群优化算法的最大功率跟踪

最大功率点跟踪(maximum power point tracking, MPPT)是光伏系统保持高效运行的有效方法。在光伏阵列发生局部遮挡时，其功率-电压曲线会出现多峰现象，传统粒子群算法(particle swarm optimization, PSO)在此情况下进行MPPT容易陷入局部最优问题，导致收敛精度降低。为解决以上问题，提出了一种二阶振荡粒子群算法应用于最大功率点跟踪，并针对多峰函数特点进行优化。在对粒子种群初始化时采用分散定位逼近极值的方式增加粒子群的全局搜索能力，提出有效的终止策略防止系统反复波动。在MATLAB/Simulink平台进行仿真对比分析的结果表明：改进算法可有效提升MPPT控制的效率和动态品质。

Maximum Power Tracking Based on Second-order Oscillatory Particle Swarm Optimization

Maximum power point tracking (MPPT) is an effective method to keep the photovoltaic system running efficiently. When the local occlusion occurs in the photovoltaic array, the power-voltage curve will have multi peaks. In this case, traditional particle swarm optimization (PSO) for MPPT is prone to fall into the local optimal problem, resulting in reduced convergence accuracy. In order to solve the above problems, a second-order oscillating particle swarm algorithm was proposed to investigate the maximum power point tracking. Optimized for the characteristics of the multimodal function. In the initialization of particle population, decentralized distribution was used to approach the extreme value to increase the global search ability of particle swarm. An effective termination strategy was proposed to prevent the repeated fluctuation of the system. The results of simulation and comparative analysis on MATLAB/Simulink platform show that the improved algorithm can effectively improve the efficiency and dynamic quality of MPPT control.