Gas in oil prediction method via combined prediction and residual correction

Abstract

The invention provides a gas in oil prediction method via combined prediction and residual correction. The method comprises the following steps: (1) separating a gas through an oil-gas separation device, analyzing gas composition and proportion by using dissolved gas online chromatographic analysis and gas sensitive sensor analysis, and establishing a dissolved gas time sequence; (2) determining the correlation between gases by using a gray correlation degree, establishing an MGM (1, n) prediction model, and outputting a gray prediction result; and (3) determining a training set and a test set, and initially correcting the training set and test set results through residual adaptive regression, performing state division on the training set on the basis, and further correcting a predicted value of the test set by using a Markov model. The gas in oil prediction method provided by the invention is mainly applied to the state monitoring of an oil-filled transformer so as to discover a faultin time and to avoid further loss conveniently, and the error caused by the corrected predicted value is obviously smaller than the error caused by the traditional gray prediction result;and accurateand efficient fault identification is ensured, the principle is simple, and the operation is simple and convenient.

Description

technical field [0001] The invention belongs to the field of prediction of dissolved gas in transformer oil, and in particular relates to a gas prediction method in oil combined with prediction residual correction. Background technique [0002] The power transformer is a pivotal device in the power system, which is responsible for converting voltage, distributing and transmitting electric energy. Once a fault occurs, it will directly affect the safe and reliable operation of the power system. However, the reality is that there is always corrosion caused by electricity, heat, machinery, humidity, etc. in the operating environment of the transformer, which will lead to gradual deterioration of its performance. Once a fault occurs, it may cause a power outage accident in the power grid. In order to ensure the safe and stable operation of the transformer, it is necessary to carry out state monitoring and fault diagnosis. [0003] Oil-filled transformers are currently used most ...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to propose a gas-in-oil forecasting method based on combined forecasting residual correction for the above-mentioned deficiencies in existing oil and gas forecasting, and use residual adaptive regression and Markov model to improve the gray forecasting method. The dissolved gas in the transformer oil is predicted to achieve the purpose of monitoring the state of the transformer and identifying the occurrence of faults in time

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