基于EEMD和特征降维的非侵入式负荷分解方法研究

汪敏; 张孟健; 禹洪波; 熊炜; 袁旭峰; 邹晓松

汪敏,张孟健,禹洪波,熊炜,袁旭峰,邹晓松.基于EEMD和特征降维的非侵入式负荷分解方法研究[J].电测与仪表,2024,61(6):80-86.
WANG Min,ZHANG Mengjian,YU Hongbo,XIONG Wei,YUAN Xufeng,ZOU xiaosong.Research on non-intrusive load decomposition method based on EEMD and feature dimensionality reduction[J].Electrical Measurement & Instrumentation,2024,61(6):80-86.

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

DOI：10.19753/j.issn1001-1390.2024.06.011

中文关键词: 非侵入式负荷分解单通道盲源分离集合经验模态分解相关性过滤主成分分析

英文关键词:non-intrusive load decomposition, single-channel blind source separation, ensemble empirical mode decomposition, correlation filtering, principal component analysis

基金项目:国家自然科学基金项目(52067004);贵州省科学技术基金项目 ([2019]1058, [2019]1128)

作者中文名	作者英文名	单位
汪敏	WANG Min	贵州大学电气工程学院
张孟健	ZHANG Mengjian	贵州大学电气工程学院
禹洪波	YU Hongbo	贵州大学电气工程学院
熊炜	XIONG Wei	贵州大学电气工程学院
袁旭峰	YUAN Xufeng	贵州大学电气工程学院
邹晓松	ZOU xiaosong	贵州大学电气工程学院

摘要点击次数: 554

中文摘要:

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。

英文摘要:

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.

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