风力发电机组运行状态的混沌特性识别及其趋势预测

通过对风力发电机组运行状态参数时间序列进行非线性动力学建模,解析了机组运行状态的动力特征,验证了风力发电机组运行状态的混沌特性。在此基础上,根据混沌时间序列的相空间重构理论,应用加权一阶局域预测方法对风电机组运行状态进行混沌预测研究,采用某风场风电机组实际运行数据对预测方法进行检验。计算结果表明,应用混沌预测方法对风力发电机组运行状态进行预测是可行的,具有较高的精度,为风电机组状态预测提供了新的思路。

Chaotic characteristics identification and trend prediction of running state for wind turbine

The nonlinear dynamic model is built for the chronological state parameter series of wind power generator set,the dynamic characteristics of its running state are analyzed and its chaotic characteristics are verified,based on which its chaotic prediction method is studied according to the phase space reconstruction theory. The proposed weighted first-order local prediction method is tested with the actual operational data of a wind power generator set and the results show that,the chaotic prediction method is feasible with higher precision.