Improved particle swarm-optimized neural network-based transformer fault diagnosis method

Abstract

The invention relates to an improved particle swarm-optimized neural network-based transformer fault diagnosis method. The method includes the following steps that: related data of dissolved gases in transformer oil and transformer fault information are obtained so as to be adopted as sample data, and a drop half-normal distribution scoring model is adopted to pre-estimate the data of the dissolved gases in the transformer oil; the network structure of a neural network is determined; and the parameters of the neural network are optimized by using an improved particle swarm algorithm; pre-estimated sample data are adopted to train the parameter-optimized neural network, so that a final neural network model can be obtained; and the neural network model is adopted to process transformer data to be evaluated, so that the fault type of a transformer can be obtained through diagnosis. With the method of the invention adopted, the interference of original data redundancy information can be reduced, and the validity of data evaluation can be improved; and convergence speed in the training of the neural network can be increased, and the search ability of parameter optimization can be improved, and the accuracy and reliability of transformer fault diagnosis can be improved finally.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to View More

AI Technical Summary

Benefits of technology

This patented technology provides methods for detecting and identifying small amounts (< 5%) of gases from various types of liquids such as petroleum products or water during manufacturing processes. By optimizing certain mathematical functions called particles with a special structure, these techniques are able to quickly distinguish between different causes of damage caused due to factors like temperature changes or contamination. These improvements make it possible to improve the accuracy and efficiency of fault detection systems while reducing their complexity.

Problems solved by technology

This patented technical problem addressed by this patents relates to studying transformation device failures that occur due to excessive oxygen content or other factors like overheating which could lead to catastrophic damage if left untrusted until repaired shortly afterward. Current methods involve analyzing specific variables from outside sources (such as temperature) while ignoring their impact on the overall healthiness of the transformed devices being monitored.

Images