基于深度军队联合作战算法的永磁同步发电机最大功率跟踪
Maximum power point tracking of permanent magnetic synchronous generator based on deep joint operation algorithm

本文提出一款新型启发式算法,即深度军队联合作战算法(deep joint operations algorithm,DJOA),用于调节永磁同步发电机(permanent magnetic synchronous generator,PMSG)的比例-积分-微分(proportional-integral-derivative,PID)控制器最优参数。从而实现不同风速下的最大功率跟踪(maximum power point tracking,MPPT)。DJOA由如下三类策略构成,即(a)进攻作战：DJOA与传统军队联合作战算法(joint operations algorithm,JOA)的进攻作战机理一致,以实现最优解的全局搜索(global exploration)；(b)深度防御作战：DJOA引入两名副官(当前两个次最优解),通过综合考虑军官(当前最优解)与两名副官的信息,从而合理引导士兵以实现更深度的局部探索(local exploitation)；(c)混合重组作战：DJOA引入混合蛙跳算法(shuffled frog leaping algorithm,SFLA)机制来有效避免算法陷入局部最优。本文通过三个算例对DJOA的优化性能进行研究,即阶跃风速、低频随机风速和高频随机风速。仿真结果表明,与量子遗传优化算法(quantum genetic algorithm,QGA)、生物地理学习的粒子群算法(biogeography-based learning particle swarm optimization,BLPSO)和JOA相比,所提算法能够最大程度地获取风能且仅需最低的控制成本。
This paper proposes a novel meta-heuristic algorithm, called deep joint operations algorithm (DJOA), which is used to optimally tune the proportional-integral-differential (PID) controller parameters for permanent magnetic synchronous generator (PMSG) to achieve maximum power point tracking (MPPT) under different wind speed. DJOA is consisted of three operations, e.g., (a)Offensive operations: DJOA adopts the same mechanism of joint operations algorithm (JOA) to achieve a global exploration; (b)Deep defensive operations: DJOA introduces two deputy officers (currently sub-optimal solutions) to achieve a deeper local exploitation through a cooperation between the officer and two deputy officers; (c)Shuffled regroup operations: DJOA employs the mechanism of shuffled frog leaping algorithm (SFLA) to effectively prevent the algorithm from trapping at a local optimum. Three cases are carried out, including step change of wind speed, low-turbulence stochastic wind variation, and high-turbulence stochastic wind variation. Simulation results demonstrate that DJOA can extract the maximum wind power and require just minimal control costs compared to that of quantum genetic algorithm (QGA), biogeography-based learning particle swarm optimization (BLPSO) and JOA