为了解决传统光伏阵列最大功率点追踪(maximum power point tracking，MPPT)算法易陷入局部最大功率点(local maximum power point，LMPP)的问题，本文提出一种基于自适应位置调节的飞蛾扑火(adaptive position adjust-ment for moth-flame optimization algorithm，AMFO)MPPT控制方法，该方法在飞蛾的位置更新机制中引入自适应位置插值策略和自适应权重因子策略，提高了算法的求解精度和优化速度，使之不易陷入局部最大功率点。将改进后的算法应用于光伏系统 MPPT 中，仿真实验结果表明:改进后的算法相较于传统的飞蛾扑火优化(moth-flame optimi-zation，MFO)算法、灰狼优化(grey wolf optimizer，GWO)算法和粒子群优化(particle swarm optimization，PSO)算法，在均匀光照和局部遮阴条件下的追踪速率和精度均有较大提升。
MPPT Control Method for Moth-Flame Optimization Based on Adaptive Position Adjustment
In order to solve the problem of traditional maximum power point tracking(MPPT)algorithm for photo-voltaic arrays being prone to getting stuck in local maximum power point(LMPP),in this article we propose an adap-tive position adjustment for moth-flame optimization algorithm(AMFO)MPPT control method based on adaptive position adjustment.This method introduces an adaptive position interpolation strategy and adaptive weight factor into the position updated mechanism of moths,thus improving the accuracy and optimization speed of the algorithm,and making it less prone to getting stuck in LMPP.The improved algorithm was applied to the photovoltaic system MPPT,and simulation results showed that compared to the traditional moth-flame optimization(MFO)algorithm,grey wolf optimizer(GWO)algorithm,and particle swarm optimization(PSO)algorithm,the improved algorithm significantly improved tracking speed and accuracy under uniform lighting and local shading conditions.
photovoltaic arraysmaximum power point tracking(MPPT)adaptive position adjustmentmoth-flame optimization algorithmlocal shading
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维普
