利用极端学习机的新型定子双馈电双凸极电机效率优化

通过励磁电流和电枢电流的协调控制，可以使电动车用定子双馈电双凸极电机在整个调速范围内运行在较高率区，以满足电动车的高效率要求。但电机中励磁电流与电枢电流、转速和转矩的非线性特性使协调控制实现困难。极端学习机作为单隐层前向神经网络的一种典型学习算法，可以有效解决这个非线性问题。该文提出基于极端学习机的励磁电流和电枢电流的协调控制技术，利用电机运行中效率最高的实验数据作为训练样本，构成两输入两输出的单隐层神经网络，对网络进行离线训练，得到隐层单元的节点数和输出权值用作在线控制。实验结果表明，该方法可以使定子双馈电双凸极电机在整个运行范围内运行在高效率区。

Efficiency Optimization of New Stator-doubly-fed Doubly Salient Motor by Using Extreme Learning Machine

Satisfy the demands of the high efficiency of electric vehicles (EVs), the stator-doubly-fed doubly salient (SDFDS) motor for EVs can run with high efficiency in the whole velocity range by the coordinate control of field current and armature current. But the nonlinear relationship among field current, armature current, torque and speed makes the realization of the coordinate control difficult. As a new learning algorithm with fast speed and good generalization, the extreme learning machine for single-hidden layer feed forward neural networks (SLFNs) can solve the nonlinear relationship effectively. Thus, a coordinated controller of field current and armature current based on extreme learning machine is proposed, in which the SLFNs are trained off-line by the experimental data of highest efficiency, which composes neural networks with single hidden layer with two inputs and two outputs. And the convergence results of training, which are nodes and output weights, are applied for on-line control afterward. The experimental results show that with the proposed scheme the SDFDS motor could obtain apparently efficiency optimization in the whole velocity range.