• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2020, Vol. 41 ›› Issue (8): 1-8.doi: 10.12204/j.issn.1000-7229.2020.08.001

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Non-intrusive Load Monitoring Method Based on PCA-ILP Considering Multi-feature Objective Function

LIU Sai1, LIU Yu2, GAO Shan2, GUO Haomin3, SONG Tiancheng2,  JIANG Weiyi1, LI Zheng1, WANG Juncheng1, XU Yimin1   

  1. 1. State Grid Jiangsu Electric Power Co., Ltd. Maintenance Branch Company, Nanjing 211102, China; 2. School of Electrical Engineering, Southeast University, Nanjing 210096, China; 3. State Grid Fujian Electric Power Co., Ltd. Xiamen Power Supply Company, Xiamen 361004, Fujian Province, China
  • Online:2020-08-07 Published:2020-08-07
  • Supported by:
    This work is supported by National Natural Science Foundation of China(No. 51907024).

Abstract: Non-intrusive load monitoring (NILM) acquires power consumption information in low cost. It can realize household load recognition and decomposition without affecting the normal power consumption. The installation of smart meters also provides data and technical support for NILM. Firstly, by researching power characteristics, current waveforms and harmonic characteristics of common household appliances, principal component analysis (PCA) is used to reduce the dimension of high-dimensional harmonic feature space and extract the main harmonic information. It is combined with basic power characteristics to form multi-feature objective function. Then, on the basis of integer linear programming (ILP) model, NILM method of PCA-ILP considering multi-feature objective function is established to realize load decomposition and recognition for different electrical appliances. Finally, case study indicates that the proposed method has a sound performance for load decomposition in different scenarios of household appliances under different signal-to-noise ratio (SNR).

Key words: non-intrusive load monitoring, principal components analysis, multi-feature objective function, integer linear programming

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