Fault diagnosis method of oil-immersed transformer based on art1 neural network

Abstract

The invention discloses an ARTI-neural network-based oil-immersed transformer fault diagnosis method. The method comprises: step one, an ARTI-neural network-based oil-immersed transformer fault diagnosis model is constructed by using a specific ARTI neural network algorithm; step two, on the basis of a four-ratio method, input and output quantities of the constructed ARTI-neural network-based oil-immersed transformer fault diagnosis model obtained by the step one are determined; step three, after the step two, a parameter of the ARTI-neural network-based oil-immersed transformer fault diagnosis model is set; step four, learning training is carried out on a fault sample by using the ARTI-neural network-based oil-immersed transformer fault diagnosis model; and step five, an identification diagnosis is carried out on an actual fault data type by using the ARTI-neural network-based oil-immersed transformer fault diagnosis model. According to the invention, with the method, real-time on-line fault diagnoses on different types of overheating faults of the oil-immersed transformer can be carried out correctly.

Description

technical field [0001] The invention belongs to the technical field of comprehensive fault monitoring methods for power transformers, and in particular relates to a fault diagnosis method for oil-immersed transformers based on an ART1 neural network. Background technique [0002] The power transformer is the most important electrical device in the power system, and it is also one of the electrical equipment that causes the most accidents in the power system. Its operating status directly affects the safety and stability of the power supply system. It is an extremely important issue concerned by the power sector to discover potential faults of power transformers in time to ensure the safe and effective operation of the transformers, thereby improving the reliability of the stable power supply of the transformers. Therefore, it is of great practical significance to study the fault diagnosis technology of power transformers and improve the operation and maintenance level of tr...

AI Technical Summary

Problems solved by technology

[0003] Although the current power transformer fault diagnosis technology has developed a variety of effective fault diagnosis methods, most of them are superimposed and piled up to some extent. Most fault diagnosis methods themselves still lack a perfect theoretical basis and systematic concept. system

In addition, most fault diagnosis methods use the characteristic signals exhibited by objects to diagnose characteristic types of faults, which often require a large number of fault training samples, and it is difficult to obtain large-scale effective fault sample data in practice.

Due to the different voltage levels, insulation structures and fault degrees of power transformers, the dissolved gas content in the transformer oil is quite random, and there is a certain amount of redundant data, which requires the fault diagnosis method to have strong fault tolerance. The research on this aspect is far from deep enough

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