A non-intrusive method for extracting charging load patterns of electric vehicles
A technology for charging loads and electric vehicles, applied in the direction of measuring electrical variables, instruments, complex mathematical operations, etc., to achieve the effects of improving charging mode extraction, low computing cost capabilities, and high estimation accuracy
Active Publication Date: 2021-08-24
SICHUAN UNIV
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[0007] Aiming at the defects in the prior art that it is difficult to decompose signals containing noise and multi-power devices, the present invention discloses a non-invasive method for extracting electric load patterns of electric vehicles. By adopting the technical solution described in the invention, the Eliminate noise and multi-power device signals, so as to ensure the accuracy of electric vehicle charging signals, and then accurately judge the charging status of electric vehicles
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[0043] This embodiment, as the basic embodiment of the present invention, discloses a method for extracting a non-invasive electric vehicle charging load pattern, which is characterized in that it includes the following steps:
[0044] A method for extracting a non-intrusive electric vehicle charging load pattern, comprising the following steps:
[0045] S1. Collect resident electric meter data, and use robust local weighted regression as a smoothing time series decomposition method to decompose the above collected electric meter data, where the decomposed smart electric meter data express
[0046] The formula is: x t =x tr,t +x se,t +x re,t t=1,...,T; (1)
[0047] x in the above expression tr,t Usually a low frequency trend component; x se,t Describe the periodic characteristics of the signal; x re,t Describes the high-frequency trend component; the low-frequency trend component is x tr =[x tr,t ,...,x tr,T ]; (2)
[0048] S2. Utilize the time series decomposition...
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The invention discloses a non-intrusive method for extracting charging load patterns of electric vehicles. The time series decomposition method using robust local weighted regression as a smoothing method is used to extract low-frequency trend components from the above-mentioned electric meter data, and then the low-frequency trend components are extracted from the low-frequency trend components by discrete wavelet transform. The feature components related to the charging load of electric vehicles are extracted from the components, and the start and end time of charging are determined through high-order difference recognition. Finally, the feature components are matched with the pre-stored charging load templates based on the start and end time, and the dynamic The time warping algorithm can obtain the shortest path from the matched similar distance matrix to realize the extraction of the electric vehicle charging load pattern; compared with the prior art, the extraction method of the present invention is simple and convenient to calculate, and can effectively avoid noise components and local The impact of mutations on the detection results can improve the accuracy of detection.
Description
technical field [0001] The invention relates to the technical field of power supply monitoring, in particular to a method for extracting a non-invasive electric vehicle charging load pattern. Background technique [0002] With the increase in the number of electric vehicles on the demand side, how to accurately identify the charging behavior of electric vehicles is the key to achieving safe and stable operation of the grid and strengthening possible ancillary services. Intrusive load-harvesting approaches to capture EV charging behavior by installing monitoring devices at home are often not practical due to additional hardware installation costs and leakage of residential energy privacy. Therefore, the non-intrusive identification method of extracting residential electric vehicle charging load patterns from smart meter data by designing algorithms has become a current research and development trend. Non-intrusive load identification is to decompose the total consumption rea...
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IPC IPC(8): G01R22/00G06F17/12
CPCG01R22/00G06F17/12
Inventor 向月王杨周润
Owner SICHUAN UNIV