Transformer Partial Discharge Identification Method
A technology of partial discharge and identification method, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem of not being able to give clustering results
Active Publication Date: 2020-09-15
SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
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The nearest neighbor propagation algorithm is only suitable for dealing with data clustering problems with compact hyperspherical structures. When the distribution of data sets is loose or the structure is complex, the algorithm cannot give ideal clustering results.
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[0074] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0075] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
[0076] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or c...
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The invention discloses a transformer partial discharge identification method based on a particle swarm optimization kernel neighbor propagation algorithm, including: building a transformer oil-paper insulation partial discharge test model; gray image moment feature, type feature and texture feature parameter extraction; principal component analysis method Dimensionality reduction of feature parameters; calculation formula based on kernel function and similarity of shared neighbors; method steps of classifier based on particle swarm optimization kernel neighbor propagation algorithm. The invention improves the disadvantage that the traditional neighbor propagation algorithm is only suitable for dealing with the data clustering problem of compact hyperspherical structure, and still has certain effectiveness when the distribution of data sets is loose or the structure is complex. The experimental results show that when applied to the identification of the four discharge types of partial discharge, the kernel neighbor propagation algorithm based on particle swarm optimization significantly improves the recognition rate compared with the traditional neighbor propagation algorithm, and improves the recognition of some model data compared with the BP neural network Rate.
Description
technical field [0001] The present invention relates to the field of electrical technology, in particular to a transformer partial discharge pattern recognition method based on particle swarm optimization kernel neighbor propagation algorithm, wherein, the neighbor propagation algorithm (Affinity Propagation, abbreviated as AP), based on particle swarm optimization kernel neighbor propagation algorithm (Kernel Affinity Propagation Algorithm Based on ParticleSwarm Optimization, abbreviated as KAP-PSO). Background technique [0002] The power transformer is an important equipment in the power system, not only because of its high cost, but also has the function of raising and lowering the voltage during the power transmission and distribution process of the power system and connecting different power grids. Once the transformer fails, it will inevitably cause a large-scale power interruption in the power system, which will affect the safe and stable operation of the power syste...
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IPC IPC(8): G06K9/62
CPCG06F18/285
Inventor 魏本刚姚周飞霍凯旋娄杰李祥耀李可军
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO