DFIG-SMES互补系统一次调频控制  

Primary Frequency Control of Doubly Fed Induction Generator-Superconducting Magnetic Energy Storage Complementary System

作　　者：柳伟[1,2] 顾伟[1,2] 孙蓉 李群 王锐[1,2] 

机构地区：[1]东南大学电气工程学院,南京210096 [2]江苏省智能电网技术与装备重点实验室,镇江212009 [3]江苏省电力公司电力科学研究院,南京211103

出　　处：《电工技术学报》2012年第9期108-116,共9页Transactions of China Electrotechnical Society

基　　金：国家高技术研究发展计划(863计划)(2011AA05A107);国家自然科学基金(50907008);江苏省自然科学基金(SBK201122387);东南大学优秀青年教师基金;东南大学重大基础科研基金资助项目

摘　　要：由于大规模风电接入,电网要求风电场具备一次调频能力,因此风机附加频率控制和风储互补运行设计日益受到关注。本文针对双馈风电机组(DFIG)和超导储能(SMES)互补系统(DFIG-SMES),提出一种基于模糊神经网络的互补频率控制策略。利用神经网络的学习推理能力训练出适应性较强的模糊神经控制器,实现双馈风电机组转子动能和备用功率联合控制;采用模糊神经网络优化超导储能有功功率控制特性,进而利用超导储能快速的功率吞吐能力和灵活的四象限调节能力为系统提供频率支撑。基于4机2区域系统对相关控制策略进行了仿真验证,结果表明所提模糊神经控制策略具有较好的鲁棒性,互补系统的联合运行能有效改善系统的频率稳定性。Due to the development of wind power, the frequency control capacity ot wma Iarm is required, so the additional frequency control and the coordination with stored energy win particular attention recently. A new fuzzy neural network combination control is proposed to doubly fed induction generator（DFIG）-superconducting magnetic energy storage（SMES） complementary system. It takes advantage of the teaming ability of neural network to form an adaptable fuzzy neural controller to control the rotor kinetic energy and reserve power of DFIG; and it also optimizes the active power of SMES to give frequency control support during sizeable frequency deviation. The performance of the control strategy is simulated based on 4 machine 2 area system, the results indicate that the proposed fuzzy neural control strategy has good robustness, and the complementary system can effectively improve the frequency control stability of system.

关 键 词：双馈感应风电机组 超导储能 一次调频 模糊神经控制 

分 类 号：TM712[电气工程—电力系统及自动化]