Two-dimensional Hankel matrix multi-scale SVD transformation method

A multi-scale, transformation method technology, applied in the field of power transmission and distribution, can solve the problems of traveling wave fault location and fault type identification failure, traveling wave detection interference, etc., to achieve accurate traveling wave detection and fault point location results, eliminate Interference, effect of removed interference

Active Publication Date: 2019-06-18
珠海妙微科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] When the transmission line fault traveling wave monitor collects data, due to the line structure and the collector itself, noise signals will be mixed into the traveling wave signal. When the traveling wave signal is weak, the noise with relatively high intensity will affect the detection of the traveling wave. Cause interference, resulting in the failure of fault location and fault type identification based on traveling waves

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  • Two-dimensional Hankel matrix multi-scale SVD transformation method
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Embodiment 1

[0056] like figure 1 As shown, the embodiment of the present invention provides a multi-scale SVD transformation method based on a two-dimensional Hankel matrix. The method is used for detecting a fault traveling wave singular point of a high-voltage transmission line. The method includes:

[0057] S1 data acquisition: collect three-phase traveling wave data for modulus conversion, and obtain line traveling wave mode signals for analysis;

[0058] S2 performs two-dimensional Hankel matrix multi-scale SVD transformation on the traveling wave line mode signal X, and obtains the approximate component P at the decomposition scale of the jth layer j and the detail component Q j , j=1,2,...;

[0059] S3 constructs composite matrix C based on S2 j , for C j After SVD transformation, calculate the singular entropy corresponding to the decomposition layer, and calculate the singular entropy increment ΔE compared with the previous layer j . Judging if ΔE j greater than a certain ...

Embodiment 2

[0097] The high-voltage transmission line fault traveling wave distance measurement method based on two-dimensional Hankel matrix multi-scale SVD in the embodiment of the present invention includes the following steps:

[0098] 11 as figure 2 As shown, according to the actual needs of the site, a traveling wave monitor is installed on both sides of the line, and there is no need for symmetrical installation. Record the position of each monitor from the reference point separately, and the two monitors adopt GPS synchronous time synchronization system. Take the sampling data of 1ms before and after the fault and send it to the data analysis module.

[0099] 12 In the data analysis module, perform modulus transformation on the three-phase traveling wave sampling data, remove the coupling between the three-phase line parameters, extract and analyze the traveling wave line mode components whose wave velocity is less affected by the line distance, and obtain the first wave of the ...

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Abstract

The embodiments of the invention disclose a two-dimensional Hankel matrix multi-scale SVD transformation method. The method includes the following steps that: S1, three-phase traveling wave data are collected, and modulus transformation is performed on the three-phase traveling wave data, and linear traveling wave mode signals are obtained, and the linear traveling mode signals are analyzed; S2, Xtransformation is performed on the traveling wave linear mode signals, and an approximate component Pj and a detail component Qj under the decomposition scale of a j-th layer are obtained; S3, a composite matrix Cj is constructed based on the S2, SVD transformation is performed on the Cj, a singular entropy corresponding to the decomposition layer is calculated, and a singular entropy increment deltaEj relative to an upper layer is calculated; if it is judged that the deltaEj is larger than a certain value epsilon, the approximate component Pj is assigned to a one-dimensional matrix X, and the step S2 is repeated, an optimal decomposition layer number r and detailed components Q1, Q2, ..., and Qr which are obtained after the decomposition of the first r layers are outputted; S4, dynamic threshold noise reduction processing is performed on a series of matrices Qi (i=1, 2, ..., and r) which are obtained after the r layers are decomposed, so that matrices which are represented by a symbol in the descriptions of the invention are obtained; and S5, transformation is performed on traveling wave signals X', and head wave and reflected wave time points are determined according to a singular point position in a traveling wave signal approximate component Qr which is obtained according to the decomposition of an r-th layer; and S6, a fault location interval is determined according to the head wave time of each monitor, and therefore, a fault point can be accurately located.

Description

technical field [0001] The invention relates to the field of power transmission and distribution, in particular to a multi-scale SVD transformation method based on a two-dimensional Hankel matrix. Background technique [0002] As a long-distance power transmission channel, high-voltage transmission lines span long distances and have complex geographical environments along the way. They are inevitably affected by factors such as lightning, tree barriers, ice, and wildfires. These factors will lead to varying degrees of line insulation performance degradation, thereby Cause the transmission line fault to trip. When a transmission line fails, a traveling wave signal propagating along both sides of the line will be generated at the fault point. The traveling wave fluctuates at a speed close to the speed of light on the line, and is not affected by factors such as line operation mode, line length, and transition resistance. . Therefore, through accurate detection and analysis o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/08
Inventor 林顺富曹雨顾乡刘丹凤刘持涛徐征顾春艳高健飞畅国刚吕乔榕许亮峰
Owner 珠海妙微科技有限公司
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