Research on Synchronous Control of Double-Point Fatigue Loading System for Wind Turbine Blades
In order to solve the problem of unsynchronized vibration of two shakers in the full-size double-point fatigue test of wind turbine blades,a hybrid control strategy of GA-Adam-BP neural network and tra-ditional PID is used,and the switching boundary value is introduced to judge the attribution of control pow-er.Based on the global search ability of genetic algorithm for BP neural network to initialize the screening of weights and thresholds,the Adam algorithm is used to realize the dynamic learning rate of BP neural net-work,which effectively reduces the oscillation of the learning route and makes the convergence time shor-ter.Simulink simulation model is established and test platform is built for validation.The simulation and ex-perimental results show that the motor speed error under hybrid control is below 3%,the master-slave excit-er phase difference range is±1.3° compared to BP neural network.The hybrid control exhibits higher ac-curacy and online operation capability with good robustness and anti-interference capability,and achieves better synchronization control between the blade dual-point fatigue test shakers.
wind turbine bladefatigue loadingGA algorithmAdam algorithmneural networkhybrid control
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