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机构地区:[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070 [2]西安铁路职业技术学院电气工程系,陕西西安710014
出 处:《传感器与微系统》2010年第11期43-47,共5页Transducer and Microsystem Technologies
摘 要:针对传统矢量控制精度依赖于发电机精确数学模型和参数等缺点,在分析双馈感应发电机电磁关系基础上,研究一种基于模糊神经网络的励磁控制策略。设计了基于模糊RBF解耦的励磁控制器,并对模糊RBF解耦算法进行研究;对基于模糊RBF的励磁控制的兆瓦级双馈风力发电系统进行仿真,结果验证了基于模糊RBF励磁控制的双馈发电机系统具有有功、无功功率的快速解耦能力,其调节过程响应速度快,超调量小,无静差;对于外界干扰和电机内部参数变化等问题都有良好的鲁棒性和参数自适应性。Aiming at the shortcomings of traditional vector control' s precision which depends on accurate mathematical models and parameters of generator, the fuzzy-RBF network control strategy of excitation power is studied based on analysis of the relationship between power balance and the electromagnetic of DFIG. The excitation controller to decouple is designed based on fuzzy-RBF. The algorithm of fuzzy-RBF deeouple is studied in detailed. The results show that the doubly-fed generator system based on fuzzy-RBF excitation control has the capacity of fast decouple of active power, reactive power, fast response, small overshoot, and no static error. The results also show that the control system is prior to the traditional method on robustness and parameter adaptive for the interference external and the electromagnetic internal the parameters change .
分 类 号:TM614[电气工程—电力系统及自动化]
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