To ensure the energy efficiency and stability of the SRDM-based WTs across the entire wind speed ranges,a control method was proposed based on radial basis function(RBF)neural net-works and sliding mode variable structure control(SMVSC),which enabled precise and rapid pitch an-gle adjustment for SRDM-based WTs,while considering the effects of wind wheel imbalance,wind shear,and tower shadow.This approach incorporated the sliding mode error into the adaptive law,al-lowing for the effective suppression of chatting effects by dynamically adjusting the weights and center values of the RBF neural network in real-time.A simulation model of 1.5 MW SRDM-based WTs was established,and then verified using the built experimental platform,through which the control per-formance of the proposed RBF-SMVSC method was compared and validated.The results indicate that,compared to independent pitch methods with traditional proportional-integral(PI)and SMC,the pro-posed control method may adjust WT's speed and power output more rapidly and accurately under va-rious wind speed conditions,and significantly enhance energy capture and reduce unbalanced loads.
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