基于EEMD和特征降维的非侵入式负荷分解方法研究

Research on non-intrusive load decomposition method based on EEMD and feature dimensionality reduction

针对现有非侵入式居民用电负荷监测缺乏对独立负荷完整、全面的分解方法,导致用电信息的完整性得不到保证的不足,提出一种基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)和Pearson-PCA改进的盲源分离算法。利用EEMD对总功率信号分解,以消除经验模态在分解过程中易出现模态混叠的现象,并得到一系列固有模式函数(intrinsic mode functions,IMF)。结合Pearson相关系数和主成分分析法(principal component analysis,PCA),提出Pearson-PCA改进算法对IMF进行降维,剔除相关性较弱的IMF分量,以及估计源信号数目。运用快速独立分量分析(fast independent component analysis,FastICA)对降维后的IMF进行分解,计算得出源功率信号。将提出的改进算法应用于非侵入式居民用电负荷分解问题,采用能量分解数据集(reference energy disaggregation data,REDD)进行实验仿真。实验结果表明:在不同用电场景下,提出的改进算法均具有较好的分解效果。

Aiming at the lack of a complete and comprehensive decomposition method for independent load in the existing non-intrusive residential electricity load monitoring,the integrity of electricity consumption information cannot be guaranteed.An improved blind source separation algorithm based on ensemble empirical mode decomposition(EEMD)and Pearson-PCA is proposed.Firstly,EEMD is used to decompose the total power signal to eliminate the modal aliasing phenomenon in the empirical mode decomposition process,and it can obtain a series of intrinsic mode functions(IMF).Secondly,combining with Pearson correlation coefficient and principal component analysis(PCA),an improved Pearson-PCA algorithm is proposed to reduce the dimensionality of the IMF,remove the weaker IMF components,and estimate the number of source signals.Then,fast independent component analysis(FastICA)is used to decompose the reduced-dimensional IMF to calculate the source power signal.Finally,the proposed improved algorithm is applied to the non-intrusive residential electricity load decomposition problem,and the reference energy disaggregation data(REDD)is used for experimental simulation.The experimental results show that the proposed improved algorithm has a better decomposition effect in different electricity consumption scenarios.