基于SVR风力机变桨距双模型切换预测控制

TWO MODEL SWICHED PREDICTIVE PITCH CONTROL FOR WIND TURBINE BASED ON SUPPORT VECTOR REGRESSION

通过预测控制算法对风力机变桨距系统进行调节,风力机非线性模型采用支持矢量回归(SVR)算法进行拟合。考虑实运转风力机模型的变化,通过改进的序列最小优化(SMO)代替原增量SVR的凸二次规划算法,并用偶然点排除和模型储存复用等新方法,有效缩短在线运算时间,实现了改进型增量学习算法的在线辨识。此外,由于电液比例变桨距执行机构的差动回路设计和风力负载的单方向性,造成桨叶顺桨和逆桨时系统模型并不一致,本研究的预测控制过程采用双模型切换。整个算法在变桨距风力机半物理仿真试验台上进行试验,当风速高于额定风速时,与常规的PID控制和单模型SVR预测控制相比,发电机输出最大功率误差分别从约9％和10％降低到3％左右。

Model predictive control arithmetic is used for wind turbine pitch control, whose nonlinear model is identified by support vector regression （SVR）. But wind turbine＇s model may be changed under fieldwork, so incremental learning algorithm is adopted for SVR online identification. The improved sequential minimal optimization （SMO） algorithm is used to substitute the original quadratic programming （QP）. And the algorithm is further improved by the method that the invalid break points are eliminated and the model is stored and reused. So the calculation time of SVR online identification is greatly shorted. Because the differential circuit is used in the electro-hydraulic proportional pitch-controlled system and the direction of load is changeless, the model is different between feathering and backpaddling. Therefore the two models are switched in the predictive control course. Then when wind speed is above the rated, the generator power is kept more steadily around the rated and the pitch load fluctuation is greatly reduced by the algorithm which is used in the pitch-controlled wind turbine semi-physical simulation test-bed than traditional PID control one.