The invention provides a wind generating set
system identification method based on the
radial basis function (RBF) neural network technique. The wind generating set
system identification method comprises the following steps that 1, data required by
system identification are obtained, specifically, the input data and the output data which are required by identification are obtained according to the characteristics of a wind generating set system, the sampling time selects the system internal sampling time, an input
signal is the generator torque Tg during torque loop identification and is the
paddle pitch angle beta during
propeller pitch loop identification, and the output data are the generator rotation speed
omega; and 2,
system identification is conducted based on the RBF technique, specifically, the wind generating set system is described, a torque loop or a
propeller pitch loop is set as a nonlinear SISO system, a nonlinear extension autoregressive East China average model NARMAX is adopted for conducting describing, and the RBF neural network training process comprises the following steps that when a
signal is forwards propagated, RBF neural
network output is calculated, and when an error is reversely propagated, the weights among various
layers of an RBF network are adjusted by adopting the
delta learning
algorithm. The wind generating set
system identification method based on the RBF neural network technique has good operation speed, low calculation amount and good stability.