首页> 外文会议>Energy and Environment Technology, 2009. ICEET '09 >Synthesized Power and Frequency Control Strategies Based on Fuzzy Neural Networks for Wind Power Generation Systems
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Synthesized Power and Frequency Control Strategies Based on Fuzzy Neural Networks for Wind Power Generation Systems

机译:基于模糊神经网络的风力发电系统综合功率和频率控制策略

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Due to its great potential value in theory and application, synthesized power and frequency control strategies of nonlinear wind power generation systems, especially combining with intelligent control methods, have been a focus in the academe. A synthesized power and frequency control method based on fuzzy neural networks presents nonlinear systems in this paper. The controller parameters were designed to detect the power and frequency fluctuation, and adaptive updating method was introduced to estimate and tracking error. Fuzzy neural networks was used to adjust the system parameters and construct automated power and frequency control, and the tracking error compensation control force, which given by state estimation, was used to realize adaptive power and frequency control. This framework leaded to a simple structure, an accurate detection and a high robustness. The simulation results in a wind power generator control system showed that it could work well with high dynamic performance and control precision under the condition of system parametersȁ9; variation and load torque disturbance.
机译:由于其在理论和应用上的巨大潜力,非线性风力发电系统的综合功率和频率控制策略,特别是与智能控制方法相结合,已成为学院的重点。本文提出了一种基于模糊神经网络的综合功率和频率控制方法,提出了非线性系统。设计控制器参数以检测功率和频率波动,并引入自适应更新方法来估计和跟踪误差。利用模糊神经网络调整系统参数,构建自动化的功率和频率控制,利用状态估计给出的跟踪误差补偿控制力,实现功率和频率的自适应控制。该框架导致简单的结构,准确的检测和高度的鲁棒性。风力发电机控制系统的仿真结果表明,在系统参数ȁ9的条件下,该系统具有良好的动态性能和控制精度。变化和负载转矩扰动。

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