磁控开关型故障限流器RBF神经网络自适应偏置电流控制

RBF Neural Network Based Adaptive Bias Current Control in Magnetic-Controlled Switcher Type Fault Current Limiter

基于新型磁控开关型故障限流器偏置电流控制系统复杂的非线性时变特性,提出一种基于径向基函数神经网络的限流器偏置电流自适应控制系统,采用2个RBF神经网络来分别构成自适应控制网络和辨识网络,实现了偏置电流的实时控制.试验证明,利用该偏置电流控制系统,限流器的响应速度和限流效果可以得到保证.

Due to the strong nonlinearity and multivariable characteristic of novel magnetic-controlled switcher type fault current limiter （FCL） bias current control system, an approach of adaptive bias current control for the FCL based on radial basis function（RBF） neural network was presented. This method builds up an adaptive control RBF neural network can achieve the bias current real time control, and and current limiting effect can be guaranteed when and an identification RBF neural network. The system the experimental results prove that the response speed using the orooosed control system.