基于神经网络的双馈感应发电机滑模控制

针对不平衡电网下双馈感应发电机运行不佳的问题,将神经网络控制和二阶滑模控制相结合构成的神经网络滑模控制器运用到双馈风力发电机的直接功率控制中。设计了二阶滑模控制器,二阶滑模能够有效地削弱传统滑模控制的抖振；接着,设计了径向基神经网络对系统的不确定部分进行逼近；最后,基于李雅普诺夫稳定性定理推导了神经网络权值更新律,证明了控制系统的稳定性。仿真结果表明所设计控制策略能对有功、无功功率及其定子电流进行有效控制,削弱了传统滑模控制中的抖振。

Sliding Mode Control of Doubly Fed Induction Generator Based on Neural Network

Aiming at the poor operation of doubly fed induction generator under unbalanced grid voltage, neural network sliding mode controller, which combined neural network control with secondorder sliding mode control, was applied to direct power control of doubly fed wind generator. A secondorder sliding mode controller was designed. The secondorder sliding mode could effectively weaken the chattering of traditional sliding mode control. At the same time, a radial basis function neural network was designed to approximate the uncertain part of the system. Finally, based on Lyapunov stability theory, the adaptive law of the neural network weight was deduced, and the stability of the control system was proved. The simulation results showed that the proposed control strategy could effectively control the active power, reactive power and stator current, and weaken the chattering in traditional sliding mode control.