基于多策略混合改进MVO算法的光伏多峰MPPT研究

Photovoltaic multi-peak MPPT via multi-strategy hybrid improved MVO algorithm

光伏阵列的电功率输出在局部遮荫工况下具有多峰特性,且随外界环境的变化而变化.为实现高效电能输出,利用多元宇宙优化(MVO)算法在解决低维度、小规模优化问题中的突出优势进行最大功率点跟踪(MPPT),并融合多种策略对其存在的缺陷进行改进.采用拉丁超立方抽样策略初始化宇宙种群,并对按照轮盘赌策略随机交换的宇宙实施柯西变异,提高宇宙种群的多样性.同时引入莱维飞行式量子粒子群优化(QPSO)算法,且对虫洞存在概率及旅行距离率进行自适应调整,以增强算法的全局勘探及局部开发能力.Matlab仿真结果表明,相比其他算法,采用该算法的MPPT时间减少了45%以上,精度亦有所提高,从而具有更优的MPPT性能,可有效提高光伏发电效率.

The electric power output of photovoltaic array exhibits multi-peak characteristics under partial shading conditions,and changes with the external environment.To achieve efficient power output,the Multi-Verse Optimization(MVO)algorithm,which has outstanding advantages in solving low dimensional and small-scale optimization problems,is exploited to carry out Maximum Power Point Tracking(MPPT),and multiple strategies are integrated to address its defects.Then Latin hypercube sampling is used to initialize the universe population,and Cauchy mutation is carried out on the universe randomly swapped according to roulette strategy,thus increasing the diversity of the universe population.Meanwhile,the Levy flight Quantum Particle Swarm Optimization(QPSO)algorithm is introduced,and the wormhole existence probability and travel distance rate are adaptively adjusted to enhance the global exploration and local development capabilities of the algorithm.Simulation on Matlab shows that the proposed approach reduces MPPT time by more than 45%and improves MPPT accuracy,indicating its better MPPT performance to improve the photovoltaic power generation efficiency.