Composite Model-free Adaptive Predictive Control for Wind Power Generation Based on Full Wind Speed

Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.

基于补偿函数观测器的三相并网逆变器无模型预测功率控制

针对三相并网逆变器受参数变化等因素引起的未知扰动导致系统控制性能下降的问题,提出一种基于补偿函数观测器的无模型预测功率控制算法。首先,以超局部模型代替原有的逆变器模型,降低对系统模型的依赖,提升系统鲁棒性;其次,设计补偿函数观测器,估计超局部模型中的集总扰动,利用含补偿函数的信号跟踪器补偿传统观测器的估计残差,提高系统对扰动的估计精度;最后,设计无差拍代价函数,实现系统的无差跟踪。实验结果验证了所提方法具有更好的动稳态性能。