Transformer fault identification method based on kernel function extreme learning machine

Abstract

The invention provides a transformer fault identification method based on a kernel function extreme learning machine, and the method comprises the steps: obtaining a vibration signal as an analysis sample under the operation condition of a transformer, carrying out the noise elimination of the vibration signal, obtaining each frequency energy characteristic value of the vibration signal based on wavelet packet decomposition and reconstruction, extracting each frequency energy characteristic value as a fingerprint vector, dividing the fingerprint vectors into two sample sets, namely a training set and a test set, establishing an optimized kernel function extreme learning machine network model, performing model training by utilizing a fingerprint vector training set, inputting a to-be-tested set into the model, analyzing, calculating and outputting a check result, obtaining a working state of the transformer, and achieving fault identification of the transformer. According to the method, the abnormal problem that the system is caught in dimensionality disaster is solved, the performance of a fault judgment analysis result is further optimized and improved, and automatic identification of the abnormal fault of the power transformer is achieved.

Description

technical field [0001] The present application relates to the technical field of transformer fault identification, in particular to a transformer fault identification method based on a kernel function extreme learning machine. Background technique [0002] With the increasing capacity of transformers in the domestic power grid, the deformation of transformer winding damage is also on the rise. In order to effectively reduce the risks and impacts of transformer faults, transformer fault identification technologies and methods are used to detect and monitor the deformation and damage of windings in a timely manner. The degree of damage can effectively prevent the further deterioration of the transformer accident. In the long-term theoretical research and engineering practice, there have been some technical methods and achievements in transformer fault identification. At present, there are mainly three types of faults in power transformers: (1) insulation faults; (2) power fau...

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Problems solved by technology

[0004] This application provides a transformer fault identification method based on a kernel function extreme learning machine to solve the problems of insufficient time monitoring of winding faults and insufficient accuracy of judgment of deformation after short circuit in traditional technology

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