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Transformer fault diagnosis method based on hard voting ensemble learning

A transformer fault and integrated learning technology, applied in the direction of integrated learning, instrument, character and pattern recognition, etc., can solve the problem of low accuracy of fault diagnosis, achieve high accuracy of fault identification, reduce intensity, simple and efficient fault identification

Pending Publication Date: 2021-11-30
ZAOZHUANG POWER SUPPLY COMPANY OF STATE GRID SHANDONG ELECTRIC POWER +1
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Problems solved by technology

However, in practical applications, the accuracy of diagnosis can only reach about 80%, and the accuracy of fault diagnosis is low.

Method used

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  • Transformer fault diagnosis method based on hard voting ensemble learning

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Experimental program
Comparison scheme
Effect test

Embodiment

[0059] 1. Establish an ensemble learning classification model for transformer fault diagnosis;

[0060] Data set acquisition: 133 groups of gas components in transformer oil during normal and fault conditions are obtained from the equipment operation and maintenance management system as sample data, and each group of sample data contains five types of methane, ethylene, ethane, acetylene, and hydrogen Gas composition information and its corresponding transformer fault type;

[0061] Divide 133 sets of sample data into training set and test set, and divide them into 93 sets of training data and 40 sets of test data according to the ratio of 7:3;

[0062] Support vector machine, logistic regression, nearest neighbor classification, Bayesian classification, decision tree and random forest are used to learn the training data to obtain the fault diagnosis model, and the test data is used to verify the model of each fault diagnosis model, and the results of each model are obtained. ...

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Abstract

The invention discloses a transformer fault diagnosis method based on hard voting ensemble learning. The method comprises the following steps of: step 1, establishing a hard voting ensemble learning classification model for performing fault diagnosis on a transformer; and step 2, carrying out fault type identification on unknown transformer faults. According to the method, the fault diagnosis model of the transformer is built by adopting the machine learning, the fault identification accuracy is high, the fault identification is simple and efficient, effective support is provided for initial transformer operation state identification, the working intensity is greatly reduced, and the safety and reliability of transformer fault type identification are guaranteed.

Description

technical field [0001] The invention relates to the technical field of transformer fault diagnosis, in particular to a method for transformer fault diagnosis based on hard voting ensemble learning. Background technique [0002] As an important part of the power system, transformers play a vital role in the safe and reliable supply of power. Once a fault occurs, it will seriously affect the safe and stable operation of the power grid. As the connection and conversion part of electric power, the internal structure and operating conditions of the transformer are relatively complex, and it is difficult to accurately conduct fault research and judgment when a fault occurs. Therefore, the fault diagnosis of the transformer is of great significance to ensure the safe operation of the power grid. At present, the gas-in-oil analysis method (IEC three-ratio method) is mainly used for transformer fault diagnosis, which can accurately and reliably discover latent faults that gradually d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/28G06K9/62G06N20/20
CPCG01N33/28G06N20/20G06F18/24G06F18/214
Inventor 马洪斌杨飞史娜
Owner ZAOZHUANG POWER SUPPLY COMPANY OF STATE GRID SHANDONG ELECTRIC POWER
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