基于自适应变分模态分解的组合模型风电功率预测

Wind Power Prediction by Combined Model Based on Adaptive Variational Mode Decomposition

风电机组出力的高波动与随机性,影响电力系统安全稳定运行与风电预测精度,针对此提出结合风电功率波动特性研究的风电功率预测方法。首先从时间与机组规模尺度分析风电功率波动特性,并指导选取合适的风电数据用于风电功率预测;然后建立基于最小二乘支持向量机的风电机组短期功率预测模型,采用自适应变分模态分解实现风电数据分频,并采用改进粒子群优化最小二乘支持向量机模型中影响回归预测的模型参数。实验结果表明,预测模型自适应性较强,通过预测误差评价指标,可证明预测方法的有效性。

In view of the high fluctuation and randomness of wind turbine output,which affects the safe and stable operation of power system as well as the accuracy of wind power prediction,a wind power prediction method based on the fluctuation characteristics of wind power is proposed.First,the fluctuation characteristics of wind power are analyzed in terms of time scale and unit scale,and the appropriate wind power data is selected for wind power prediction.Then,a wind turbine short-term power prediction model based on least squares-support vector machine(LS-SVM)is established.The adaptive variational mode decomposition(AVMD)is used to decompose the wind power data to achieve frequency division,and the improved particle swarm optimization(IPSO)is used to optimize the model parameters affecting the regression prediction in the LS-SVM model.Experimental results show that the prediction model has strong adaptability,and the effectiveness of the prediction method can be proved by prediction error evaluation indexes.