Transformer state parameter data prediction method and system based on fruit fly algorithm optimization

Abstract

The invention discloses a transformer state parameter data prediction method based on fruit fly algorithm optimization. The transformer state parameter data prediction method based on the fruit fly algorithm optimization comprises the following steps: S100, obtaining transformer state parameter data in a period of time, and converting the transformer state parameter data into a transformer state parameter matrix in a matrix form, wherein the transformer state parameter includes related data of the transformer state parameter; S200, constructing a transformer state parameter data prediction model, obtaining a hyper-parameter of the prediction model based on the fruit fly algorithm, and training the prediction model based on the transformer state parameter matrix; and S300, predicting the transformer state parameter data based on the transformer state parameter data prediction model trained by the step S200. The transformer state parameter data prediction method based on the fruit fly algorithm optimization can avoid the hyper-parameter selection falling into local convergence, thereby improving the training efficiency of the prediction model and ensuring the higher prediction accuracy and reliability of the transformer state parameter data. In addition, the invention also discloses a corresponding transformer state parameter data prediction system based on the fruit fly algorithm optimization.

Description

technical field [0001] The invention relates to a transformer state parameter prediction method and system in the field of power system power transmission and transformation equipment operation and maintenance, in particular to a transformer state parameter prediction method and system based on fruit fly algorithm optimization. Background technique [0002] As the main core equipment of the power transmission and transformation system, the power transformer is of great significance to ensure its healthy and stable operation. Usually by monitoring the operating status of the transformer and predicting its change trend, it is possible to effectively prevent transformer failures, make a good plan, and ensure the stable operation of the transformer. In order to effectively monitor the operating state of the transformer and predict its change trend, it is usually necessary to monitor and predict the various transformer state parameters that reflect the operating state of the tran...

AI Technical Summary

Problems solved by technology

[0006] The prediction model based on the neural network can be used to predict the transformer state parameter data, but there is a problem that the selection of hyperparameters is easy to fall into local convergence, which leads to low training efficiency of the prediction model and affects the prediction accuracy and reliability.

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