电网技术2017，Vol.41Issue(9)：3033-3040,8.DOI:10.13335/j.1000-3673.pst.2016.2780
基于信号稀疏化欠定求解的居民用户非侵入式负荷分解算法
Non-Intrusive Residential Load Decomposition Algorithm Solving Underdetermined Equations Based on Signal Sparsification
摘要
Abstract
Non-intrusive residential load monitoring is a key technology for smart power. Load decomposition is an important part of realization of the technology. This paper studies a load decomposition algorithm resolving problem of morbid equation with current signals based on non-intrusive acquisition mode. Starting time points for different electrical appliances are different. Taking this as background, the solving model is set up to separate two signals from acquisition signals in unit time interval. One is mixed signal produced by previous device and another is independent running current signal produced by newly added load. With this model, each load put into service can be decomposed independently. It can ensure small underdetermined dimensionality and be solved effectively. In solution process, combined with sparsification of the load current signals in frequency domain, morbid equation is transformed into an optimization constraint. And the two signals are recovered from the mixed signal with two step iterative shrinkage threshold algorithm. Then similarity coefficient is calculated to estimate whether signal separation is successful. In order to verify effectiveness of the algorithm, real sampling load data are used. With positive iteration method, present power load is obtained. According to similarity coefficient, load types can be determined.关键词
非侵入负荷分解/稀疏性/欠定求解/相似系数Key words
non-intrusive load decomposition/sparsity/solving underdetermined equations/similarity coefficient分类
信息技术与安全科学引用本文复制引用
武昕,韩笑..基于信号稀疏化欠定求解的居民用户非侵入式负荷分解算法[J].电网技术,2017,41(9):3033-3040,8.基金项目
国家重点研究发展计划项目(2016YFB0901104) （2016YFB0901104）
中央高校基本科研业务费专项资金资助项目(2016MS13). Project Supported by the National Key Research and Development Program of China (2016YFB0901104) （2016MS13）
the Fundamental Research Funds For the Central Universities (2016MS13). （2016MS13）
