基于改进鲸鱼优化算法的光伏发电系统MPPT控制研究

Research on MPPT Control of Photovoltaic Power Generation System Based on Improved Whale Optimization Algorithm

在局部阴影条件下,常规的最大功率点跟踪MPPT(maximum power point tracking)算法因含有容易陷入局部极值、跟踪精度低等弊端,使其无法及时、精确地跟踪光伏发电系统的最大功率点,因此,提出了一种基于改进型鲸鱼优化算法的光伏发电系统MPPT控制策略。首先,采用混沌映射初始化种群,增加种群的多样性。其次,通过引入非线性收敛因子使局部寻优能力和全局搜索能力达到均衡。最后,通过引入非线性时变的自适应权重使系统及时跳出局部最优解,并提高搜索的精度。经仿真验证,与粒子群优化算法、狮群优化算法、传统的鲸鱼优化算法等相比,改进的鲸鱼算法在跟踪速度、精度、稳定性等方面均有更显著的效果。

Under the condition of local shadow,the conventional maximum power point tracking(MPPT)algorithm has disadvantages such as vulnerability to falling into local extreme values and low tracking accuracy,which makes it impossible to track the maximum power point ofa photovoltaic(PV)power generation system quickly and accurately.Therefore,an MPPT control strategy for the PV power generation system based on an improved whale optimization algorithm(WOA)is proposed.First,the population is initialized with a chaotic map to increase its diversity.Second,the local optimization capability and global searchingcapability are balanced by introducing a nonlinear convergence factor.Finally,by introducing a nonlinear time-varying adaptive weight,the system can jump out of the local optimal solution in time and improve the searching accuracy.It is verified by simulation results that compared with the particle swarm optimizationalgorithm,lion swarm optimization algorithm and the traditional WOA,the improved WOAhas more significant effects in terms of tracking speed,accuracy and stability.