Maximum power point tracking of permanent magnetic synchronous generator based on deep joint operation algorithm

被引：1
作者：
Yang B. [1 ]
Zhu D.-N. [1 ]
Qiu D.-L. [1 ]
Shu H.-C. [1 ]
Yu T. [2 ]
机构：
[1] Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, Yunnan
[2] College of Electric Power, South China University of Technology, Guangzhou, 510640, Guangdong
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Deep joint operations algorithm; Maximum power point tracking; Permanent magnetic synchronous generator; Wind energy conversion system;
D O I：
10.7641/CTA.2018.80333
中图分类号：
学科分类号：
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, high-turbulence stochastic wind variation and robustness test. 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, as well as the greatest robustness in the presence of generator parameter uncertainties. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1283 / 1295
页数：12
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