文章摘要
基于改进粒子群算法的风机频率控制研究
Coordinated optimization of wind turbine generator frequency control based on improved particle swarm optimization
投稿时间:2019-06-09  修订日期:2019-09-09
DOI:
中文关键词: 风力发电  虚拟惯量控制  下垂控制  协调控制  粒子群优化算法
英文关键词: wind power generation  virtual inertia control  droop control  coordination control  particle swarm optimization
基金项目:
作者单位E-mail
游广增 云南电网有限责任公司 misteryou@qq.com 
杭志   
陈凯   
刘超   
钱迎春   
李常刚   
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中文摘要:
      高风电占比系统中,风电机组参与系统频率调节时,同时使用虚拟惯量控制与下垂控制会出现风机调频功率相互竞争的问题。为提高风机出力,优化系统的调频效果,本文对风电机组虚拟惯量控制和下垂控制进行协调优化。首先构建了风机参与调频的优化控制策略求解模型,对粒子群优化算法(Particle swarm optimization, PSO)进行了适应性改造:根据搜索代数对惯性权重ω、自我学习因子c1和社会学习因子c2进行动态调整;加入变异操作。基于改进的粒子群优化算法优化控制参数,得到风机参与调频的协调控制策略,且对风速和故障程度等因素具有较好的适应性。最后以某省级电网为算例,仿真验证了控制策略以及改进算法的有效性。
英文摘要:
      When virtual inertia control and droop control are used simultaneously in high proportion wind power system, there will be frequency regulation power competition problems. In order to improve the frequency regulation effect of the system, this paper coordinates and optimizes the virtual inertia control and droop control of wind turbines. A model of coordinated control strategy is constructed for wind turbines participating in frequency regulation. The particle swarm optimization algorithm is improved:according to search algebra, inertia weightω、self learning factor c1 and social learning factor c2 are dynamically adjusted;and mutation operation is added. Based on the improved particle swarm optimization algorithm, the control parameters are optimized. The coordinated control strategy of wind turbine participating in frequency regulation is obtained. Finally, a provincial power grid is taken as an example to verify the effectiveness of control strategy and improved algorithm.
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