|
Abstract: 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.
Key words:Model
predictive control Pitch-controlled SVR SMO Semi-physical
CLC No: TK83
国家863计划(2001AA512020)和国家自然科学基金(50505043)资助项目.Received
20050607,received in revised form 20060110 |