基于频域分解和精度加权集成的分布式风电功率预测方法

Distributed Wind Power Forecasting Method Based on Frequency Domain Decomposition and Precision-weighted Ensemble

针对具有强随机波动性的分布式小风电开展功率高精度预测研究,对增强小风机自身稳定性和配电网支撑力具有重要意义。为此,提出了基于频域分解和精度加权集成的分布式风电预测方法。首先,通过自适应噪声完备集合经验模态分解将原风电信号分解到不同频段,捕捉风电局部波动特征;然后,构建具有双层异质学习器的精度加权Stacking,实现多模型优势互补,增加应对分布式风电强波动特性的泛化能力;最后,通过4台风机的实际数据验证了所提方法优于当前用于大型风电场、风电集群及单风机的几种先进预测方法,证明了所提方法针对分布式风电功率预测的有效性和泛化性。

Research on high-precision forecasting for distributed small wind power with strong random fluctuations is of great significance for enhancing the stability of small wind turbines and providing reliable support for distribution.Therefore,a distributed wind power forecasting method based on frequency domain decomposition and a precision-weighted ensemble was proposed.First,the original wind power signal was decomposed into different frequency bands through complete ensemble empirical mode decomposition with adaptive noise to capture the local fluctuation characteristics of wind power.Then,a precision-weighted Stacking model with two-layer heterogeneous learners was constructed to fully utilize the performance advantages of different learners and increase the generalization ability to deal with strong fluctuations.Finally,the model was verified on actual dataset from four wind turbines.It was observed that the proposed method is superior to several advanced prediction methods currently used in large wind farms,wind farm clusters,and single wind turbines,which proves the validity and generalization of the proposed method for distributed wind power forecasting.