Correction method for on-line monitoring noisy data of oil chromatography

Abstract

The invention relates to a correction method for on-line monitoring noisy data of oil chromatography. The method includes the following steps: step 1, collecting data of off-line tests and on-line monitoring of the oil chromatography; step 2, obtaining an optimal combination of significant parameters in a regression model of a support vector machine through a firefly algorithm; step 3, training the support vector machine with the small amount of accurate off-line test data of the oil chromatography obtained, and obtaining the regression model of the support vector machine; step 4, initializing a permissible deviation radius h of the on-line monitoring data, calculating a piecewise function between the off-line tests, and judging whether the on-line monitoring data of the oil chromatography is in a permissible error range of the model; step 5, correcting the on-line data; and step 6, according to the result of correction feedback of the on-site data, adjusting the parameters in the model. When the method is used for correction of the on-line data of the oil chromatography, the effect is stable, the result is accurate, the time is short, and the real-time performance is good, and the method is very suitable for correction of the one-site on-line data of the oil chromatography.

Description

technical field [0001] The invention belongs to the technical field of on-line monitoring of substation equipment, is applied in the process of correcting noise data of transformer on-line monitoring equipment, and specifically relates to a method for correcting noise data on-line monitoring of oil chromatography. Background technique [0002] Transformer oil chromatographic on-line monitoring can grasp the operating status of the transformer in time, discover and track latent faults, and provide guarantee for the reliable operation of the transformer. However, because the online monitoring of oil chromatography is easily affected by factors such as ambient temperature, humidity, and the errors of the monitoring equipment itself, the online data may be distorted, and data calibration is required before status evaluation and fault diagnosis. At present, domestic and foreign scholars have done a lot of research work on the data correction problem and proposed some algorithms. ...

AI Technical Summary

Problems solved by technology

Principal component regression analysis can effectively remove noise data, but the fitting error is large and the correction accuracy is low; the neural network algorithm has a good fitting effect, but when the amount of data is large, the training time is long, and there is a problem of "over-learning"

[0003] Aiming at the current situation that the online data correction effect of oil chromatography is poor, the present invention proposes a data correction method based on firefly support vector machine

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