Gathered grey wolf optimizer based optimal control of doubly-fed induction generator
DOI：10.19783/j.cnki.pspc.191074
Key Words:gathered grey wolf optimizer  doubly-fed induction generator  optimal control
Author NameAffiliation
ZHAO Ruifeng 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 
GUO Wenxin 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 
WANG Bin 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 
PAN Zhenning 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 
LI Shiming 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 
LI Bo 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 
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 
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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).
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