Review on Event Inspection Based Non-intrusive Load Monitoring Algorithms

被引:0
|
作者
Bao H. [1 ]
Yang S. [2 ]
Chen Z. [1 ]
Guo X. [3 ]
Li J. [2 ]
机构
[1] Nanning Power Supply Bureau of Guangxi Power Grid Company Limited, Nanning
[2] Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, Guangxi University, Nanning
[3] Electric Power Research Institute of Guangxi Power Grid Company Limited, Nanning
关键词
device identification; digital power grid; evaluation indices; event detection; feature extraction; non-intrusive load monitoring;
D O I
10.7500/AEPS20220713005
中图分类号
学科分类号
摘要
Non-intrusive load monitoring (NILM) can realize the automatic monitoring of equipment-level power consumption data, which is one of the most important technologies in the sensing and measurement process of digital power grid construction. This paper reviews the research of event inspection based NILM algorithms. Firstly, the event inspection based NILM algorithm is defined, and the technical differences between the event inspection based NILM algorithms and the combinatorial optimized NILM algorithms are compared. After that, the typical steps and methods of the event inspection based NILM algorithms are sorted and reviewed. And then, following the sequence of the process of event inspection based NILM algorithms, the key technologies to improve the performance of the event inspection based NILM algorithms are sorted out from three perspectives of event detection, feature extraction, and device identification. Finally, the common performance evaluation indicators of event inspection based NILM algorithms are summarized and the future research directions of event inspection based NILM algorithms are prospected. © 2023 Automation of Electric Power Systems Press. All rights reserved.
引用
收藏
页码:94 / 109
页数:15
相关论文
共 80 条
  • [1] Peng LI, XI Wei, CAI Tiantian, Et al., Concept,architecture and key technologies of digital power grids[J], Proceedings of the CSEE, 42, 14, pp. 5002-5017, (2022)
  • [2] HART G W., Nonintrusive appliance load monitoring [J], Proceedings of the IEEE, 80, 12, pp. 1870-1891, (1992)
  • [3] CHENG Xiang, LI Linzhi, WU Hao, Et al., A survey of the research on non-intrusive load monitoring and disaggregation[J], Power System Technology, 40, 10, pp. 3108-3117, (2016)
  • [4] DENG Xiaoping, ZHANG Guiqing, WEI Qinglai, Et al., A survey on the non-intrusive load monitoring[J], Acta Automatica Sinica, 48, 3, pp. 644-663, (2022)
  • [5] GUO Hongxia, LU Jinwei, YANG Ping, Et al., Review on key techniques of non-intrusive load monitoring[J], Electric Power Automation Equipment, 41, 1, pp. 135-146, (2021)
  • [6] WU Xin, YAN Meng, GUO Yifan, Et al., Non-intrusive load identification by combined support vector machine based on structured characteristic spectrum[J], Automation of Electric Power Systems, 46, 12, pp. 210-219, (2022)
  • [7] QI Bing, LIU Liya, WU Xin, Et al., Power signal disaggregation for smart meter based on graph signal processing[J], Automation of Electric Power Systems, 43, 4, pp. 79-85, (2019)
  • [8] LUO Ping, FAN Xingchi, ZHANG Jianmin, Et al., Nonintrusive load decomposition based on operation state of electrical appliance and deep learning[J], Automation of Electric Power Systems, 45, 12, pp. 49-56, (2021)
  • [9] IEEE PES General Meeting, (2010)
  • [10] TSAI M S., Modern development of an adaptive non-intrusive appliance load monitoring system in electricity energy conservation[J], Applied Energy, 96, pp. 55-73, (2012)