基于LGWO和扰动观察复合算法的MPPT研究

MPPT Research Based on LGWO and Perturbation Observation Compound Algorithm

为提高光伏发电效率,提出了一种复合算法解决局部阴影情况下光伏阵列输出功率呈现多峰值状态的问题,弥补了传统方法的不足。该方法将传统的灰狼算法优化,引入莱维飞行模块、加入贪婪策略,加强了算法的全局搜索能力和快速性。在搜索至光伏发电最大功率点附近时切换为扰动观察法,利用其快速收敛的特性迅速找到最大功率点,提升了光伏阵列的输出功率。通过Simulink仿真实验,验证了所提出复合算法优越的跟踪性能,在准确性与快速性上较传统灰狼算法均有显著提升。

In order to solve the problem of multi-peak output power of photovoltaic power generation generated by local shadows, a compound algorithm combining gray wolf optimization algorithm and disturbance observation method is proposed. Levy flight module and greedy strategy are embedded into the grey wolf algorithm to achieve optimization. The composite algorithm makes use of the accuracy of gray wolf optimization algorithm and the rapidity of disturbance observation method to accurately track the maximum power point of photovoltaic power generation under multiple peak values. The simulation results are verified by Simulink experimental platform. The Simulation results show that the proposed algorithm has better tracking performance than the traditional gray Wolf algorithm in accuracy and rapidity.