Solid insulation life prediction method based on transformer-related operation data

Abstract

The invention discloses a solid insulation life prediction method based on transformer related operation data. The method comprises the following steps: 1) obtaining and storing transformer solid insulation life prediction related data; 2) reading related data for numbering; 3) preprocessing the data; 4) obtaining the closest distribution model by using a Monte Carlo method; 5) training the data by using the BP neural network to obtain a prediction model; 6) enabling the BP neural network to perform network initialization; 7) randomly selecting an adjusting parameter by adopting a Monte Carlomethod, and updating the loss function by adjusting a small amount of the parameter; and 8) updating the loss function of the solid insulation life prediction model through a Monte Carlo method to finally obtain life prediction of the line transformer solid insulation, thereby preventing the BP neural network from falling into the dilemma of a local minimum value, and meanwhile, having the advantages of simple structure and flexible training, and effectively improving the accuracy of transformer solid insulation evaluation.

Description

technical field [0001] The invention belongs to the technical field of power transformer solid insulation life prediction, and in particular relates to a solid insulation life prediction method based on transformer-related operating data. Background technique [0002] The power transformer is the most important equipment in the power system, and its operating status is directly related to the safe operation of the system. With the aging of power transformers and the reduction of remaining life, various faults will follow. Therefore, the prediction of the aging status and remaining life of the transformer can effectively prevent the occurrence of transformer faults, improve the service life of the equipment, and ensure the stability of the power system. [0003] The aging and remaining life of the transformer mainly depends on the aging of the solid insulation. As the aging degree of solid insulation deepens, the mechanical and electrical strength of its insulation will dec...

AI Technical Summary

Problems solved by technology

As a widely used intelligent algorithm, the artificial neural network algorithm has been used in the life prediction of solid insulation, but it has the problems of insufficient accuracy and easy to fall into local optimum

Images