Transformer state quantity data prediction method and system based on kernel principal component analysis optimization

Abstract

The invention discloses a transformer state quantity data prediction method based on kernel principal component analysis optimization; the method comprises the following steps: S100: acquiring transformer state quantity data within a period of time, and converting the transformer state quantity data into a transformer state quantity matrix in a matrix form, wherein the transformer state quantity comprises relevant data of the transformer state quantity; S200: screening the transformer state quantity matrix X based on the kernel principal component analysis algorithm, and reserving the main state quantity to obtain a transformer main state quantity matrix; S300: constructing a transformer state quantity data prediction model and training the prediction model based on the transformer main state quantity matrix; and S400: predicting the state quantity data of the transformer based on the transformer state quantity data prediction model trained in the step S300. According to the method, the model training complexity can be reduced, and therefore high prediction accuracy of the transformer state quantity data is guaranteed. In addition, the invention further discloses a corresponding prediction system.

Description

technical field [0001] The invention relates to a transformer state parameter prediction method and system in the field of operation and maintenance of power transmission and transformation equipment in a power system, in particular to a transformer state parameter prediction method and system based on nuclear principal component analysis and 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 th...

AI Technical Summary

Problems solved by technology

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 model training complexity is high, which affects the prediction accuracy

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