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

Xin Wu, Dian Jiao, Kaixin Liang and Xiao Han

Energy, 2019, vol. 188, issue C

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.

Keywords: Non-intrusive load monitoring; Online identification; Load capacitance and electricity sensibility; Periodic current (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:188:y:2019:i:c:s0360544219317062

DOI: 10.1016/j.energy.2019.116012

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