基于RBF神经网络的双馈风电机组最大功率追踪控制方法  

Research on maximum power tracking control method based on RBF neural network

作　　者：莫文水 MO Wenshui(Guangxi Modern Polytechnic College,Guangxi Hechi 547000,China)

机构地区：[1]广西现代职业技术学院,广西河池547000

出　　处：《工业仪表与自动化装置》2024年第3期105-110,共6页Industrial Instrumentation & Automation

基　　金：2023年度广西高校中青年教师科研基础能力提升项目“基于区块链与边缘计算的车联网数据安全传输关键技术研究”(2023KY1471)。

摘　　要：双馈风电机组通过转子回路进行功率调节控制,当系统在最大功率点附近工作时,回路电流会出现高频振荡的问题。这种振荡会影响系统稳定性,最大功率追踪控制是解决该问题的核心手段。提出基于径向基函数(Radial Basis Function,RBF)神经网络的双馈风电机组最大功率追踪控制方法。分析双馈风电机组基本结构与运行原理,获取风电机组输出功率,并将其输入RBF神经网络输入层中,通过网络隐含层对其展开优化训练,再通过输出层输出最大功率。采用二阶滑模控制策略与比例-积分-微分(Proportion Integration Differentiation,PID)控制器相结合的方法对发电机转矩展开追踪控制,实现对双馈风电机组最大功率的追踪控制,使风电机组保持最大功率运行。实验结果表明,所提方法追踪效果好、追踪精度高、控制精度高。The doubly fed wind turbine operates through a rotor circuit for power regulation control.When the system operates near the maximum power point,the circuit current will experience high-frequency oscillations.This oscillation will affect system stability,and maximum power tracking control is the core means to solve this problem.Propose a maximum power tracking control method for doubly fed wind turbines based on radial basis function(RBF)neural networks.Analyze the basic structure and operating principle of doubly fed wind turbines,obtain the output power of the wind turbine,and input it into the input layer of the RBF neural network.Optimize and train it through the hidden layer of the network,and then output the maximum power through the output layer.The method of combining second-order sliding mode control strategy with proportional integration differentiation(PID)controller is used to track the torque of the generator,achieving maximum power tracking control for doubly fed wind turbines and maintaining maximum power operation.The experimental results show that the proposed method has good tracking effect,high tracking accuracy,and high control accuracy.

关 键 词：双馈风电机组 追踪控制 RBF神经网络 二阶滑模控制策略 PID控制器 

分 类 号：TM614[电气工程—电力系统及自动化]