Speed Sensorless Power Optimization Control Of Wave Power System

Ocean wave energy is renewable,pollution-free,widely distributed,long-lasting and predictable,and is an extremely important new energy source in the future.However,it also has randomness and time-varying problems.The use faces huge challenges.This paper takes direct-drive permanent magnet synchronous wave power generation system as the research object,and studies the optimal power tracking control strategy of wave energy and speed sensorless control technology.First,considering the harsh working environment of the speed position sensor in the wave power system,a speed sensorless scheme based on the extended Kalman filter algorithm is proposed.Based on the stator resistance,voltage and current information of the linear motor,based on the two stages of prediction and update,an extended Kalman filter observer system was established to estimate the speed and position of the power generation system.Second,predicting the wave excitation force can reduce the delay error in the system power optimization tracking control,but the traditional BP neural network prediction algorithm is easy to fall into the local optimal and generalization ability.Therefore,an improved particle swarm optimization neural network scheme is proposed to dynamically adjust the learning factors and add mutation operators.Secondly,in order to reduce the error caused by the delay in the system power optimized tracking control,the wave excitation force is predicted.However,the traditional BP neural network algorithm applied to the prediction of wave excitation force is prone to fall into the local optimum and insufficient generalization ability.Therefore,an improved particle swarm optimization neural network algorithm is proposed,which dynamically adjusts the learning factors and adds mutation operators.The indirect prediction strategy is adopted,and the piecewise calculation idea and the wave excitation force conversion formula are used to obtain the wave excitation force prediction value.Adopt indirect prediction strategy,apply the calculation idea of segmentation and wave excitation force conversion formula to obtain the predicted value of wave excitation force.By analyzing the phase-amplitude characteristics of the wave energy conversion device,the optimal electromagnetic force corresponding to the maximum wave energy power under the regular wave is determined as the system control target,and the system control is achieved by the space vector modulation current control strategy.According to the irregularity and disturbance of the actual wave,the high-frequency ripple generated by the chattering and switching in the sliding mode variable structure control,the Fast Fourier Transform(FFT)is used to analyze the wave excitation force spectrum,and the vector Superimposed construction of maximum wave energy capture and tracking conditions.The adaptive fast terminal sliding mode variable structure control method,combined with the Lyapunov function,is used to analyze system stability,improve anti-jamming capability,quickly converge and reduce chattering,and achieve optimal power state tracking control.Finally,based on the Matlab-simulink environment,the FFT-based direct drive wave power generation speed sensorless power optimization control system under irregular waves is built.The simulation results show that the FFT can meet the optimal tracking requirements of unknown irregular waves,the control scheme has good dynamic characteristics,and the speed and position observation errors are small.The improved particle swarm optimization neural network algorithm can effectively overcome the deficiencies of traditional algorithms and improve the prediction accuracy.The adaptive fast terminal sliding mode variable control structure can realize fast tracking of the expected value,accurate tracking and strong robustness.