| 引用本文: | 赵瑞锋,郭文鑫,王 彬,等.基于群灰狼优化算法的双馈感应电机最优控制[J].电力系统保护与控制,2020,48(14):150-158.[点击复制] |
| ZHAO Ruifeng,GUO Wenxin,WANG Bin,et al.Gathered grey wolf optimizer based optimal control of doubly-fed induction generator[J].Power System Protection and Control,2020,48(14):150-158[点击复制] |
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| 摘要: |
| 设计了一种新颖的群灰狼优化算法(Gathered Grey Wolf Optimizer, GGWO)，用于整定双馈感应电机(Doubly-fed Induction Generator, DFIG)的比例-积分控制器(Proportional-integral, PI)最优参数，从而实现变风速下的最大功率点跟踪(Maximum Power Point Tracking, MPPT)并提高系统的故障穿越能力(Fault Ride-through, FRT)。GGWO在原始灰狼优化算法(Grey Wolf Optimizer, GWO)的基础上引入分组机制，将灰狼分为相互独立的合作狩猎组和随机侦察组。其中，随机侦察组中的灰狼负责进行广泛的全局搜索，而合作狩猎组的灰狼实现深度的局部探索。同时，设计狼群间的角色互换机制，可根据当前适应度函数，在下次迭代中对不同分工的狼进行角色互换，进而平衡全局搜索和局部探索的矛盾。通过阶跃风速、随机风速和电网电压跌落三个算例对GGWO的优化性能进行了研究。仿真结果表明，与遗传算法、粒子群算法、飞蛾扑火算法和GWO相比，所提算法具有更好的全局收敛性、MPPT精确性和FRT能力。 |
| 关键词: 群灰狼优化算法 双馈感应电机 最优控制 |
| DOI：10.19783/j.cnki.pspc.191074 |
| 投稿时间：2019-09-03 |
| 基金项目:中国南方电网有限责任公司科技项目资助(GDKJXM 20172831) |
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| Gathered grey wolf optimizer based optimal control of doubly-fed induction generator |
| ZHAO Ruifeng,GUO Wenxin,WANG Bin,PAN Zhenning,LI Shiming,LI Bo,LU Jiangang |
| (1. Electric Dispatch and Control Center, Guangdong Power Co., Ltd., Guangzhou 510060, China;
2. College of Electric Power, South China University of Technology, Guangzhou 510640, China) |
| Abstract: |
| A novel Gathered Grey Wolf Optimizer (GGWO) is proposed in this paper for the optimal proportional-integral (PI) parameters tuning of Doubly-Fed Induction Generator (DFIG), so as to achieve Maximum Power Point Tracking (MPPT) and to improve Fault Ride-Through (FRT) ability. Based on original Grey Wolf Optimizer (GWO), the grey wolves are divided into independent cooperative hunting group and random scout group. The grey wolves in the random scout group are responsible for extensive global search, while those in the independent cooperative hunting group are responsible for a deep local exploration. Moreover, a role reversal mechanism is developed, such that the role of different wolves in different groups can be exchanged during the next iteration, according to the current fitness function to balance the contradiction between global search and local exploration. Three case studies are carried out, including step change of wind speed, stochastic wind speed, as well as power grid voltage drop. Simulation results verify that, compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Moth-Flame Optimization (MFO) and GWO, the proposed method has better global convergence, more accurate power tracking and better FRT capability than other meta-heuristic algorithms.
This work is supported by Science and Technology Project of China Southern Power Grid Co., Ltd. (No. GDKJXM20172831). |
| Key words: gathered grey wolf optimizer doubly-fed induction generator optimal control |
