袁博, 葛少云, 刘洪. 压缩感知在非侵入式负荷监测中的应用展望[J]. 中国电机工程学报, 2024, 44(16): 6416-6431. DOI: 10.13334/j.0258-8013.pcsee.230255
引用本文: 袁博, 葛少云, 刘洪. 压缩感知在非侵入式负荷监测中的应用展望[J]. 中国电机工程学报, 2024, 44(16): 6416-6431. DOI: 10.13334/j.0258-8013.pcsee.230255
YUAN Bo, GE Shaoyun, LIU Hong. Application Prospect of Compressed Sensing in Non-intrusive Load Monitoring[J]. Proceedings of the CSEE, 2024, 44(16): 6416-6431. DOI: 10.13334/j.0258-8013.pcsee.230255
Citation: YUAN Bo, GE Shaoyun, LIU Hong. Application Prospect of Compressed Sensing in Non-intrusive Load Monitoring[J]. Proceedings of the CSEE, 2024, 44(16): 6416-6431. DOI: 10.13334/j.0258-8013.pcsee.230255

压缩感知在非侵入式负荷监测中的应用展望

Application Prospect of Compressed Sensing in Non-intrusive Load Monitoring

  • 摘要: 随着压缩感知(compressed sensing,CS)在智能电网中的应用不断深入,其在非侵入式负荷监测(non-intrusive load monitoring,NILM)领域的研究表现出滞后性。为此,在分析NILM中的CS应用必要性后,该文针对CS在NILM中的应用研究进行展望和探索。首先,对CS原理与NILM流程进行融合分析,提出压缩感知在非侵入式负荷监测中的3种应用模式;然后,针对3种应用模式的具体流程,展望各应用模式的研究方向和适用场景。在此基础上,从CS要素和负荷分析两个方面,重点探讨CS在NILM中应用所需解决的关键技术,设计适应NILM的测量矩阵、稀疏基和重构算法等CS要素的改进思路,提出在CS框架下事件探测、负荷分解、负荷识别、特征提取等负荷分析方法的实现思路。该文所做工作旨在探索CS在NILM中的应用,以期为后续研究提供指导。

     

    Abstract: With further application of compressed sensing (CS) in smart grid, research on CS application in non-invasive load monitoring (NILM) is lagging behind. For this reason, after analyzing application necessity of CS in NILM, this paper provides an outlook and exploration into the untapped application research of CS in NILM. First, three application modes of CS in NILM are proposed by comparing the principle of CS and the process of NILM. Then, based on the specific processes of those three application modes, the research directions in theory and suitable scenarios in engineering for each application mode are prospected. On this basis, the key technologies that need to be addressed in the application of CS in NILM are mainly discussed from two aspects which are CS elements and load analysis. The research ideas for improving CS elements such as measurement matrix, sparse basis and reconstruction algorithm adapted to NILM are deeply discussed, while the implementation idea of load analysis methods under CS framework such as event detection, load decomposition, load identification and feature extraction are proposed. The work done in this paper aims to realize the preliminary exploration of the application of CS in NILM, which provides guidance for follow-up research.

     

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