Research on maximum power tracking control method based on RBF neural network
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-fre-quency 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 op-erating 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 net-work,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.
double fed wind turbinestracking controlRBF neural networksecond order sliding mode control strategyPID controller