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A fault line selection method for distribution network based on k-means cluster analysis

A distribution network fault and cluster analysis technology, applied in the direction of fault location, etc., can solve the problems of low correct rate of line selection, complex problem of fault line selection, etc., and achieve the effect of high fault tolerance

Active Publication Date: 2018-03-06
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Judging from the current operation of small current grounding line selection devices, the correct rate of line selection devices in many areas is very low, which fully demonstrates the complexity of fault line selection problems and the necessity of new method research

Method used

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  • A fault line selection method for distribution network based on k-means cluster analysis
  • A fault line selection method for distribution network based on k-means cluster analysis
  • A fault line selection method for distribution network based on k-means cluster analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] Example 1: Now select fault points every 2km along the overhead line, every 1km on the cable line, and form 318 fault sample data under the condition that the transition resistance is 20Ω and the fault initial phase angle is 90°. The data length is 5ms. now assume l 1 A phase-to-ground fault occurs 1km away from terminal M, the initial phase angle of the fault is 10°, and the transition resistance is 20Ω.

[0040] (1) The two types of cluster centers obtained by the k-means cluster analysis method are the unfaulted centers C 1 , fault center C 2 . where C 1 =(4.476,0.2806),C 2 =(15.347,3.1574). Analysis results such as figure 2 shown.

[0041] (2) After the test data is decomposed by db10 wavelet, its transient zero-sequence current energy and comprehensive wavelet relative energy entropy are calculated, and the fault line is judged according to the Euclidean distance between the test data and the two cluster centers.

[0042] which is

[0043]

[0044] In...

Embodiment 2

[0046] Example 2: Now select fault points every 2km along the overhead line, every 1km on the cable line, and form 318 fault sample data under the condition that the transition resistance is 20Ω and the fault initial phase angle is 90°. The data length is 5ms. now assume l 1 A phase-to-ground fault occurs 14km away from terminal M, the initial phase angle of the fault is 90°, and the transition resistance is 200Ω.

[0047] (1) The two types of cluster centers obtained by the k-means cluster analysis method are the unfaulted centers C 1 , fault center C 2 . where C 1 =(4.476,0.2806),C 2 =(15.347,3.1574). Analysis results such as figure 2 shown.

[0048] (2) After the test data is decomposed by db10 wavelet, its transient zero-sequence current energy and comprehensive wavelet relative energy entropy are calculated, and the fault line is judged according to the Euclidean distance between the test data and the two cluster centers.

[0049] which is

[0050]

[0051] ...

Embodiment 3

[0053] Embodiment 3: Now select fault points every 2km along the overhead line, every 1km on the cable line, and form 318 fault sample data under the condition that the transition resistance is 20Ω and the fault initial phase angle is 90°. The data length is 5ms. now assume l 2 A phase-to-ground fault occurs 3km away from terminal M, the initial phase angle of the fault is 30°, and the transition resistance is 20Ω.

[0054] (1) The two types of cluster centers obtained by the k-means cluster analysis method are the unfaulted centers C 1 , fault center C 2 . where C 1 =(4.476,0.2806),C 2 =(15.347,3.1574). Analysis results such as figure 2 shown.

[0055] (2) After the test data is decomposed by db10 wavelet, its transient zero-sequence current energy and comprehensive wavelet relative energy entropy are calculated, and the fault line is judged according to the Euclidean distance between the test data and the two cluster centers.

[0056] which is

[0057]

[0058]...

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Abstract

The invention relates to a fault line selection method of a distribution network based on k-means cluster analysis, specifically setting fault locations along the lines in a resonant grounding system, and obtaining fault current curve clusters as sample data by electromagnetic transient simulation, and selecting faults The zero-sequence current in the last 5ms is decomposed by 6 layers of wavelet using db wavelet to calculate the total energy of the transient zero-sequence current in the full frequency band; at the same time, the relative entropy of the comprehensive wavelet energy is calculated, and the total The two dimensions of energy and relative entropy of comprehensive wavelet energy are used as the measure to characterize the fault characteristics, and they are mapped to the two-dimensional plane; then the clustering center of the above data on the two-dimensional plane is calculated by using the k-means clustering analysis algorithm , and then in the clustering space, the faulty lines form a clustering center, and the unfaulty lines form a clustering center. After the line selection element is faulty and started, take the fault current data in the 5ms time window as the test sample, and according to the test data and the two The Euclidean distance of the cluster centers is used to determine whether the line is faulty.

Description

technical field [0001] The invention relates to a fault line selection method of a distribution network based on k-means cluster analysis, and belongs to the technical field of power system fault line selection. Background technique [0002] As the scale of the distribution network continues to grow, the number of lines continues to increase, and the number of cable lines and cable mixed lines is also increasing. When a single-phase fault occurs, the current of the grounding capacitor also increases, and the fault occurs for a long time. If the fault cannot be eliminated in time, the equipment will be damaged, and in severe cases, it will cause serious accidents such as shutdown of power plant units and interruption of process flow, which will destroy the safe operation of the system. [0003] For a long time, due to the weak fault current and unstable fault arc, etc., the single-phase ground fault of the neutral point through the arc suppression coil grounding system often ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/08
Inventor 束洪春高利
Owner KUNMING UNIV OF SCI & TECH