Partial discharge pattern recognition method based on mixed neural network algorithm

Abstract

The invention discloses a partial discharge pattern recognition method based on a mixed neural network algorithm, the method comprises a judge process including extracting ultrahigh frequency partial discharge signal characteristic and establishing a value set library and discharge types; the method specifically comprises: establishing a typical partial discharge model firstly, acquiring a discharging ultrahigh frequency signal, and performing mixing frequency and reducing frequency process; then generating 5 kinds of two-dimension spectra according to the discharge signal, and extracting 37 kinds of statistical characteristic quantity (value set) to form the value set library; finally comparing the value set library and a value set corresponding to the statistical characteristic quantity calculated by a fault signal through the mixed neural network algorithm, and recognizing the partial discharge type of a transformer. According to the invention, the signal of partial discharge ultra wide band is fully ultilized, the single neural network pattern recognition problem is overcome, the partial discharge value set library of the transformer is established to perform pattern recognition for different kinds of discharging, the valuable data is provided for transformer partial discharge on-line monitor, and the method has good practical engineering value.

Description

technical field [0001] The invention relates to the field of power transformer fault diagnosis, in particular to a partial discharge pattern recognition method based on a hybrid neural network algorithm. Background technique [0002] There are many types of discharge in the transformer insulation structure, and different discharge types have very different effects on insulation damage, so it is necessary to distinguish various discharge types. Different types of insulation internal defects can be distinguished by pattern recognition of the discharge spectra of various typical transformer partial discharge models extracted by the partial discharge UHF measurement system. The traditional recognition and classification methods have low recognition reliability and practicability, and the more common methods are statistical probability classification and distance classification. The statistical probability classification method is to calculate the probability confidence interval...

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Problems solved by technology

The BP algorithm is relatively successful in local optimization, but it also has the following problems: 1) It is sensitive to the selection of the initial weight and threshold; 2) It is easy to fall into the local minimum point, resulting in the failure of the learning process; 3) The algorithm converges slowly and the efficiency is low. Low

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