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Power supply line fault identification method based on quantum variation multivariate universe optimization

A quantum variation and fault identification technology, applied in design optimization/simulation, instruments, artificial life, etc., to achieve the effects of ensuring stable operation, avoiding the expansion of accidents, and good convergence accuracy and speed

Pending Publication Date: 2022-05-06
LIAONING TECHNICAL UNIVERSITY
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional neural network learning algorithms need to manually set a large number of network training parameters, and it is easy to generate local optimal solutions

Method used

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  • Power supply line fault identification method based on quantum variation multivariate universe optimization
  • Power supply line fault identification method based on quantum variation multivariate universe optimization
  • Power supply line fault identification method based on quantum variation multivariate universe optimization

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Embodiment Construction

[0042] Below in conjunction with accompanying drawing and embodiment, the specific embodiment of the present invention is described in further detail;

[0043] A power supply line fault identification method optimized by quantum variation multiverse, such as figure 1 shown, including the following steps:

[0044] S1: collecting transmission line fault voltage signals;

[0045] S2: carry out variational modal decomposition to fault voltage signal; Select the mean value of the instantaneous frequency of the modal component after the variational modal decomposition, draw the normalized instantaneous frequency mean value curve, obtain the optimal K value in the variational modal decomposition algorithm;

[0046] S3: use the VMD method to decompose the fault voltage signal to obtain the IMF component; then use PE to calculate the multi-scale entropy value of each eigenmode component;

[0047] S4: the permutation column entropy value of each eigenmode component obtained by calcula...

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Abstract

The invention discloses a power supply line fault identification method based on quantum variation multivariate universe optimization, and relates to the field of power supply line fault identification. A quantum particle swarm optimization algorithm is introduced into a traditional multivariate cosmic optimization algorithm, and the average optimal position of particles is introduced into the algorithm, so that the algorithm has better convergence precision and speed; a Cauchy-Gaussian mutation strategy is adopted to solve the problem of local optimal stagnation caused by rapid assimilation of individuals in the later stage of iteration of a traditional multivariate universe optimization algorithm; the short-circuit fault is effectively identified, so that good fault data information is provided for maintenance personnel, the short-circuit fault of a line is removed in time, accident expansion is avoided, and stable operation of a power system is ensured.

Description

technical field [0001] The invention relates to the field of fault identification of power supply lines, in particular to a power supply line fault identification method optimized by quantum variation multiverse. Background technique [0002] Coal is still the most important energy source in my country. Coal is the main energy consumed in the production and life of our people. Its mining work is often accompanied by frequent safety accidents. Among them, the coal mine power supply line is one of the parts with the highest failure rate. This is mainly due to external forces, line Aging and other effects; in order to speed up the speed of the protection device to restore normal operation after clearing the fault, it is very important to determine the fault location as soon as possible; in the mine power supply line, visual inspection is very time-consuming, which delays the maintenance of the faulty line. , the accuracy of the fault location method depends on the quality of data...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/00
CPCG06F30/27G06N3/006
Inventor 付华管智峰包力铭刘尚霖陈子林周文铮
Owner LIAONING TECHNICAL UNIVERSITY
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