Evaluation method of random error between on-line monitoring data and live detection data of oil-immersed transformer

Abstract

The invention discloses an evaluation method of a random error between oil-immersed transformer on-line monitoring data and on-line detection data. The method comprises 1, respectively acquiring on-line monitoring data and on-live detection data, and pre-processing the data to obtain two time series, 2, calculating the difference of the two time series to obtain an error series, carrying out decomposition to obtain multiple intrinsic mode functions, and building a random error series from the intrinsic mode functions meeting noise characteristics, 3, decomposing the on-line monitoring time series into multiple intrinsic mode functions, removing the intrinsic mode functions meeting noise characteristics so that a real signal series, constructing an error reference series, calculating xt and xt' according to formulas of xt=St+Nt and xt'=St+Nt', and calculating corresponding signal-noise ratios dt and dt', and 4, calculating an evaluation coefficient rho of the random error according to a formula of rho=na / n.

Description

technical field [0001] The invention relates to the field of signal analysis, in particular to an evaluation method for random errors between on-line monitoring data and electrification detection data of an oil-immersed transformer. Background technique [0002] There are two types of monitoring methods for oil-immersed transformers. One is to use online monitoring, that is, use infrared spectrometers to remotely monitor equipment online; the other is live detection, that is, professionals go to the site to sample and test transformer oil. Online data has the advantages of short monitoring interval and low cost, but its data noise and random errors are often the focus of power companies. However, at present, the noise and error estimation of transformer online monitoring data are still unresolved technical problems, and the research and development of random error evaluation methods for transformer online monitoring devices is of great value. [0003] The online monitoring ...

AI Technical Summary

Problems solved by technology

These evaluation indicators are global and cannot reflect the locally available characteristics of online data

At the same time, these methods are mainly used for random error measurement of precision instruments, and are very sensitive to "gross errors (huge errors)", while the situation of random errors in transformer on-line monitoring devices is more complicated, and more universal evaluation indicators are needed

[0006] In summary, the existing methods are very limited in the case of random error estimation of transformer online data

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