Distribution transformer gear discrimination method based on least square regression

A technology of least squares and discriminant method, which is applied in data processing applications, instruments, complex mathematical operations, etc., can solve problems such as voltage fluctuations, inaccurate recognition of gear positions of distribution transformers, and inconvenient gear assignments, etc., to achieve convenience Accurate effect of gear attribution and gear recognition

Pending Publication Date: 2021-10-15
YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] This application provides a method for discriminating distribution transformer gears based on least squares regression to solve the inaccurate identification of distribution transformer gears in the prior art, which is still affected by voltage fluctuations, and it is not accurate for gear assignments. inconvenience

Method used

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  • Distribution transformer gear discrimination method based on least square regression
  • Distribution transformer gear discrimination method based on least square regression
  • Distribution transformer gear discrimination method based on least square regression

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0090] IEEE standard 14-node system simulation model such as figure 2 shown. The specific parameters are as follows: the voltage level is 10kV, the frequency is 50Hz, the transformer capacity is 10MW, and the voltage curves of different gears of the distribution transformer are as follows: Figure 4 .

[0091] The specific implementation steps are as follows:

[0092] (1) Preprocess the distribution transformer voltage curve according to step S1 in the manual, take 24 points of data for each gear for one day to form a distribution transformer voltage curve, and use Lagrangian interpolation method to interpolate the distribution transformer voltage. The comparison before and after interpolation is as follows image 3 shown.

[0093] (2) Carry out least squares regression on the data according to steps S2-S4 in the manual, and take five gear voltages. The voltage fluctuations of each gear are relatively large. If only the peak value is used to determine the gear, errors wil...

Embodiment 2

[0096] IEEE standard 14-node system simulation model such as figure 2 shown. The specific parameters are as follows: the voltage level is 10kV, the frequency is 50Hz, the transformer capacity is 10MW, and the voltage curves of different gears of the distribution transformer are as follows: Figure 5 .

[0097] The specific implementation steps are as follows:

[0098] (1) Preprocess the distribution transformer voltage curve according to step S1 in the manual, take 24 points of data for each gear for one day to form a distribution transformer voltage curve, and use Lagrangian interpolation method to interpolate the distribution transformer voltage.

[0099] (2) According to the steps S2-S4 in the manual, perform least squares regression on the cleaned data, take five gear voltages, and perform least squares regression on the 24 point voltages of each gear, and the first gear has the smallest regression voltage The value is 418.56V, the maximum value is 427.01V; the minimum...

Embodiment 3

[0102] IEEE standard 14-node system simulation model such as figure 2 shown. The specific parameters are as follows: the voltage level is 10kV, the frequency is 50Hz, the transformer capacity is 10MW, and the voltage curves of different gears of the distribution transformer are as follows: Figure 6 .

[0103] The specific implementation steps are as follows:

[0104] (1) Preprocess the distribution transformer voltage curve according to step S1 in the manual, take 24 points of data for each gear for one day to form a distribution transformer voltage curve, and use Lagrangian interpolation method to interpolate the distribution transformer voltage.

[0105] (2) According to the steps S2-S4 in the manual, perform least squares regression on the cleaned data, take five gear voltages, and perform least squares regression on the 24 point voltages of each gear, and the first gear has the smallest regression voltage The value is 423.43V, the maximum value is 424.95V; the minimum...

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Abstract

The invention provides a distribution transformer gear discrimination method based on least square regression. The method comprises the following steps: acquiring a distribution transformer voltage, and carrying out filling on missing data in the distribution transformer voltage through a Lagrange interpolation method so as to obtain target data; drawing a voltage curve according to the target data; performing least square regression on the voltage curve, and setting a first equation; establishing a function of parameters in the first equation, and calculating an expression of the parameters about the first equation so as to obtain a second equation; and calculating a least square voltage regression mean value according to the second equation, and performing archiving based on combination with a distribution transformer gear selection principle. According to the method, firstly, missing data are filled by using the Lagrange interpolation method, then a least square regression straight line is obtained by using distribution transformer outlet voltage, and finally, the least square regression mean value is calculated to realize matching with the gear to which the least square regression straight line belongs. Experimental results show that the provided discrimination method can accurately carry out gear affiliation and has certain reliability.

Description

technical field [0001] The present application relates to the technical field of electric power system dispatching, and in particular to a method for discriminating gear positions of distribution transformers based on least squares regression. Background technique [0002] In the power system, the distribution network distributes electric energy to various power users through various distribution facilities. The distribution transformer is the source of the distribution network, and its impact on the distribution network cannot be ignored. The power quality output by distribution transformers is affected by many factors, including distribution transformer gear, distribution transformer connection group, load rate, etc. Scientific analysis of these factors and formulation of reasonable treatment measures can improve power quality and power grid Economical to run. [0003] In recent years, with the continuous increase in the number of electricity users, the fluctuation of ele...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06G06F17/18
CPCG06Q10/06311G06Q50/06G06F17/18
Inventor 覃日升段锐敏姜訸马红升刑超奚鑫泽张建
Owner YUNNAN POWER GRID CO LTD ELECTRIC POWER RES INST
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