Transformer fault diagnosis method and system based on conditional generative adversarial network

Abstract

The invention discloses a transformer fault diagnosis method based on a conditional generative adversarial network. The method includes the following steps: (1) collecting transformer state historicalmonitoring data, forming a transformer fault diagnosis data set, and dividing the transformer fault diagnosis data set into a training set and a test set; (2) constructing a conditional generative adversarial network into a multi-layer sensor structure, wherein the conditional generative adversarial network has a generator and a discriminator and N outputs of the discriminator comprise: N-1 faulttypes and a judgment result about whether the data comes from analog fault data of the generator; (3) training and testing the generator and discriminator; and (4) inputting collected real-time monitoring data of the state of a transformer into the discriminator, and obtaining a fault diagnosis result of the transformer from the output of the discriminator. In addition, the invention discloses asystem based on a conditional generative adversarial network. The transformer fault diagnosis method can realize automatic identification of defects of transformer equipment.

Description

technical field [0001] The present invention relates to a fault diagnosis method and system, in particular to a fault diagnosis method and system for transformers Background technique [0002] In-depth study of power transformer fault diagnosis technology can detect latent faults of power transformers in time, accurately identify fault types and characteristics, have guiding significance for formulating appropriate maintenance strategies, and have important practical significance for reducing economic losses of power accidents and improving power grid reliability. . [0003] However, at present, due to the complex structure of the transformer, there are many factors for operating failures, and the characteristics of the failures are also difficult to summarize. There is often no one-to-one correspondence between equipment failure symptoms and failure types. Therefore, when diagnosing transformer faults, it is difficult to carry out effective and accurate diagnosis of transf...

AI Technical Summary

Problems solved by technology

[0003] However, at present, due to the complex structure of the transformer, there are many fault factors in operation, and the characteristics of the fault are difficult to summarize. There is often no one-to-one correspondence between the fault symptoms of the equipment and the fault types.

Therefore, when diagnosing transformer faults, it is difficult to carry out effective and accurate diagnosis of transformer faults if only relying on the relationship between its operating rules and the equipment operating parameters determined in advance

Although, in recent years, with the development of computer and network technology, a large amount of online data containing transformer fault information has appeared, however, the traditional method has a series of problems such as complex structure, local convergence, slow calculation speed, and low accuracy.

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