Predication method and device for top-oil temperature of transformer based on error predicting amendment

Abstract

The invention discloses a predication method and device for top-oil temperature of a transformer based on error predicting amendment, pertaining to the on-line monitoring field of the transformer. The method comprises following steps: obtaining a prediction dataset comprising load current and ambient environment of the transformer; using load current of the transformer at the current moment as input amount, utilizing a Susa thermal circuit model to predict top-oil temperature of the transformer in order to obtain top-oil temperature prediction value of the Susa thermal circuit model; normalizing top-oil temperature prediction value of the Susa thermal circuit model and the prediction dataset; using the normalized top-oil temperature prediction value of the Susa thermal circuit model and the prediction dataset as input amount and utilizing a GA-KEDLM model to make a regression forecast to obtain prediction errors; carry out anti-normalization operation on regression prediction errors; using prediction value of top-oil temperature of the Susa thermal circuit model before normalization to delete the regression prediction errors after anti-normalization operation in order to obtain top-oil temperature prediction value corrected by the transformer. The predication method and device for top-oil temperature of the transformer based on error predicting amendment are capable of accurately predicting top-oil temperature of the transformer.

Description

technical field [0001] The invention relates to the field of on-line monitoring of transformers, in particular to a method and device for predicting oil temperature at the top of a transformer based on error prediction and correction. Background technique [0002] The dynamic load capacity and insulation aging speed of power transformers mainly depend on their thermal characteristics. The top layer oil temperature is an important index to measure the thermal characteristics of the transformer, and it is also one of the important monitoring quantities during the operation of the transformer. Accurate and reliable prediction of the top layer oil temperature is of great significance for the reasonable guidance and arrangement of the dynamic load of the transformer and the prevention of thermal failure of the transformer. [0003] At present, there are many prediction methods for transformer top oil temperature, including two typical models, semi-physical models based on heat tr...

AI Technical Summary

Problems solved by technology

The former has the problems of oversimplification of the model, inaccurate parameter calculation, and great influence of environmental factors, and the model has systematic errors; the latter model has the problem of unclear physical meaning, and its generalization needs to be studied

[0004] The Susa thermal circuit model is a typical semi-physical model based on the principle of thermoelectric analogy in heat transfer. It considers the influence of oil viscosity on thermal resistance and oil time constant. It has a clear physical meaning, but it is generally targeted.

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