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

中文关键词  深度军队联合作战算法  最大功率跟踪  风能转换系统  永磁同步发电机

英文关键词  deep joint operations algorithm  maximum power point tracking  wind energy conversion system  permanent magnetic synchronous generator

中文摘要

      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。

英文摘要

      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.