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A fast online load identification algorithm based on V-I characteristics of high-frequency data under user operational constraints

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  • Wu, Xin
  • Jiao, Dian
  • Liang, Kaixin
  • Han, Xiao

Abstract

Non-intrusive load monitoring is an effective way for the power grid to obtain the power consumption on the user side. High-frequency data acquisition mode can provide more load information with a large amount of data, which is suitable for load online identification. However, high accuracy and real-time performance are required. In this regard, a fast online identification algorithm based on V-I characteristics of high-frequency is studied: According to the principle of constant capacitive and inductive characteristic of electrical appliance, under same voltage setting, the periodic current of previous switching appliance-when it is running stably-can be calculated by steady periodic current obtained each time before transient state with one-dimensional addition/subtraction. Then, the target function can be further constrained by incorporating residents’ habits, thus narrowing down the scope of possible combinations of the electrical devices that may have switched. Finally, the load states can be determined through solving the optimized function under operational constraints. This study can extract accurate and stable load currents to identify the switching load, and effectively determine the on/off time of each appliance in a short period of time. Experiments on the public BLUED dataset and laboratory data verify the effectiveness of the algorithm together.

Suggested Citation

  • Wu, Xin & Jiao, Dian & Liang, Kaixin & Han, Xiao, 2019. "A fast online load identification algorithm based on V-I characteristics of high-frequency data under user operational constraints," Energy, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:energy:v:188:y:2019:i:c:s0360544219317062
    DOI: 10.1016/j.energy.2019.116012
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    References listed on IDEAS

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    1. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
    2. Du, Gang & Lin, Wei & Sun, Chuanwang & Zhang, Dingzhong, 2015. "Residential electricity consumption after the reform of tiered pricing for household electricity in China," Applied Energy, Elsevier, vol. 157(C), pages 276-283.
    3. Fera, M. & Macchiaroli, R. & Iannone, R. & Miranda, S. & Riemma, S., 2016. "Economic evaluation model for the energy Demand Response," Energy, Elsevier, vol. 112(C), pages 457-468.
    4. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    5. Khanna, Nina Zheng & Guo, Jin & Zheng, Xinye, 2016. "Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey," Energy Policy, Elsevier, vol. 95(C), pages 113-125.
    6. Chang, Hsueh-Hsien, 2011. "Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units," Energy, Elsevier, vol. 36(1), pages 181-190.
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    Cited by:

    1. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    2. Yang, Chao & Liang, Gaoqi & Liu, Jinjie & Liu, Guolong & Yang, Hongming & Zhao, Junhua & Dong, Zhaoyang, 2023. "A non-intrusive carbon emission accounting method for industrial corporations from the perspective of modern power systems," Applied Energy, Elsevier, vol. 350(C).

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