风电功率时间序列混沌特性分析及预测模型研究

为揭示风电功率序列内在的动态特性, 利用非线性方法对风电时间序列混沌特性进行识别, 为对风电功率进行预测提供了基础.首先对某风电场的风电功率时间序列的日相关性进行了分析;然后在相空间重构的基础上计算了风电序列的最大Lyapunov指数, 验证了风电时间序列的混沌特性;由于采用Volterra滤波器多步预测法对风电功率进行超短期预测误差较大, 利用局域多步预测法以及最大Lyapunov指数法的预测结果并结合加权马尔科夫链和有序算子对Volterra滤波器的预测结果进行校正.最后以某实际风电场的风电功率预测为算例, 仿真结果表明校正预测模型有效的提高了预测精度, 其为利用Volterra滤波器多步法进行风电预测提供了有益的参考.

Chaotic characteristics analysis and prediction model study on wind power time series

In order to reveal the internal dynamic property of wind power time series the nonlinear analysis method is used to identify the chaotic property of wind power set which is the basis for the prediction of the wind power time series. Firstly day correlation property on wind power time series of a certain wind farmer is analyzed. Secondly the largest Lyapunov exponent of wind power set is calculated on the basis of phase space construction to verify the presence of chaos in wind power time series. The ultra-short-term predicted of wind power would produce larger errors by using the Volterra filter multi-step prediction so the predicted results of Volterra filter are corrected by combining the results predicted by Local-region Multi-steps Method and the largest Lyapunov exponent method with weighted Markov chain and ordered operator. Finally the prediction on wind power of a certain wind farmer is presented and the simulation results illustrate that the correction forecasting model improves high predictive accuracy effectively, which provides a useful reference for wind power prediction by the Volterra filter multi-step method.