Non-intrusive Load Identification Method Based on DTW Algorithm and Steady-state Current Waveform

被引：0
|
作者：
Qi B. [1 ]
Dong C. [1 ]
Wu X. [1 ]
Cui G. [2 ]
机构：
[1] School of Electrical & Electronic Engineering, North China Electric Power University, Beijing
[2] Electric Power Research Institute of State Grid Jiangsu Electric Power Company, Nanjing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2018年 / 42卷 / 03期
关键词：
Dynamic time warping (DTW) algorithm; Non-intrusive load identification; Steady-state waveform library of current; Steady-state waveform of load current;
D O I：
10.7500/AEPS20170103001
中图分类号：
学科分类号：
摘要：
Non-intrusive household load identification technology can guide reasonable arrangement of household users, improve energy efficiency, and provide domestic electric data support to the power sector, which is conducive to understand the load electricity law and trends for improving power planning. As steady-state characteristic values of electric load for home users are similar and irregular, most existing methods need to use advanced algorithms to train all the combinations of electrical load. Faced with the existing problems with the steady load characteristic value method and considering the unique characteristics and the superposition of steady-state waveform of domestic load, a method based on the dynamic time warping (DTW) algorithm and template library waveform is proposed to identify the household load. First of all, a template library of steady-state waveform is established. Then, under certain voltage conditions, the current steady-state waveform is measured. Finally, the DTW algorithm is used to calculate the minimum distance for identification. © 2018 Automation of Electric Power Systems Press.
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页码：70 / 76
页数：6
相关论文
共 15 条
  • [1] Dai P., Zou J., Tian J., Et al., Integrated optimization of CO<sub>2</sub> emission mitigation in China power sector, Automation of Electric Power Systems, 37, 14, pp. 1-6, (2013)
  • [2] Yu Y., Liu Y., Challenging issues of smart grid, Automation of Electric Power Systems, 39, 2, pp. 1-5, (2015)
  • [3] Liang T., Gao C., Wang B., Applications of demand side management in smart grid, Electric Power Automation Equipment, 32, 5, pp. 81-85, (2012)
  • [4] Gellings C.W., Evolving practice of demand-side management, Journal of Modern Power Systems and Clean Energy, 5, 1, pp. 1-9, (2017)
  • [5] Yan H.G., Li B., Chen S.S., Et al., Future evolution of automated demand response system in smart grid for low-carbon economy, Journal of Modern Power Systems and Clean Energy, 3, 1, pp. 72-81, (2015)
  • [6] Hart G.W., Nonintrusive appliance load monitoring, Proceedings of the IEEE, 80, 12, pp. 1870-1891, (1993)
  • [7] Qu Z., Yu H., Guo X., The recognition of appliances instantaneous load, Transactions of China Electrotechnical Society, 30, pp. 358-364, (2015)
  • [8] Gao Y., Yang H., Household load identification based on closeness matching of transient characteristics, Automation of Electric Power Systems, 37, 9, pp. 54-59, (2013)
  • [9] Yu Y.X., Li P., Zhao C.L., Non-intrusive method for on-line power load decomposition, China International Conference on Electricity Distribution
  • [10] Yu Y., Liu B., Luan W., Nonintrusive residential load monitoring and decomposition technology, Southern Power System Technology, 7, 4, pp. 1-5, (2013)
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