Winding fault locating method for power transformer based on fusion of deep convolutional neural network and visual identification

Abstract

The invention discloses a winding fault locating method for a power transformer based on fusion of the deep convolutional neural network and visual identification. The method comprises the steps of 1)enabling a power transformer to be equivalent to a two-port network, establishing a winding equivalent circuit, and calculating a transfer function of the winding equivalent circuit; 2) for a circuitwith normal circuit parameters, setting a sine wave excitation source with frequency change at one end of a power transformer winding, and obtaining an amplitude-frequency characteristic curve of each section of winding nodes in the normal state; 3) carrying out sweep frequency response analysis on the circuit in various fault states to extract amplitude-frequency characteristics, and calculatingto obtain the amplitude-frequency characteristics of each section of winding node; 4) establishing a characteristic matrix for the acquired amplitude-frequency characteristics; 5) carrying out frequency sweep response analysis on the to-be-diagnosed power transformer to form a characteristic matrix, (6) converting the characteristic matrix into an image, taking simulation and historical detectiondata as a training set, taking fault types and positions as labels, and inputting the training set and the labels into the deep convolutional neural network for training, and (7) carrying out fault classification and location on the to-be-diagnosed transformer.

Description

technical field [0001] The invention relates to a method for diagnosing and locating faults in power transformers, in particular to a method for locating faults in power transformer windings based on deep convolutional neural network fusion visual recognition. Background technique [0002] Transformer is the most important equipment in power system, and its safe and reliable operation is very important. With the continuous development of my country's power grid construction, the operating environment of transformers is becoming more and more complicated. Once a small fault occurs, it may cause incalculable losses. According to the statistical data of the international power grid working group, among the factors of transformer failure, winding deformation accounts for 30%, becoming the most important factor of transformer failure. Transformer winding faults will not have an obvious impact on equipment operation at the initial stage, but if not dealt with in time, it may lead...

AI Technical Summary

Problems solved by technology

However, these methods not only need to test different fault conditions of specific transformers, but also need to quantitatively calculate the trend analysis of fault locations through uncertain statistical parameters, which severely limits the applicability of fault diagnosis methods

Images