基于径向基函数神经网络故障观测器的风能转换系统容错控制器设计  

Design of Fault-tolerant Controller for Wind Energy Conversion System Based on the RBF Neural Network Fault Observor

作　　者：沈艳霞[1] 季凌燕[1] 纪志成[1] 

机构地区：[1]江南大学电气自动化研究所,江苏无锡214122

出　　处：《信息与控制》2015年第3期359-366,共8页Information and Control

基　　金：国家自然科学基金资助项目(61104183;61174032);高等学校博士学科点专项科研基金资助项目(20130093110011)

摘　　要：针对风能转换系统执行器部分失效故障,提出了一种新型的主动容错控制策略.应用径向基函数(radial basis function,RBF)自适应神经网络,根据系统状态观测值对执行器故障进行在线重构,基于该重构故障,设计滑模容错控制器切换增益,实现风能转换系统故障诊断与容错控制律在线整定,并进行稳定性证明.仿真结果表明,执行器发生故障时系统的功率系数和叶尖速比均能保持在最优值,从而实现额定风速以下的最大风能捕获.A novel active fault-tolerant control strategy for wind energy conversion system is proposed to deal with part failure of actuator. According to the state observer value, a self-adaptive radial basis function （ RBF ） neural network is introduced to reconstruct the actuator fault on-line. The switch gain of the sliding fault-tolerant controller is designed based on the reconstructed fault. Then a combination of both on-line fault diagnosis and tolerant control for the system is realized. Therefore, the system stability can be proven. Finally, the simulation results show that the system power coefficient and tip speed ratio can be maintained at the optimal value under the rated wind speed even if the actuator has failed, and the maximum wind energy capture can be realized.

关 键 词：风能转换系统 执行器故障 故障估计 主动容错控制 径向基函数神经网络 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]