Transformer fault diagnosis method

Abstract

The invention relates to a transformer fault diagnosis method, which comprises the following steps of S1, obtaining the concentration of dissolved gas in transformer oil and sample data correspondingto a fault conclusion, performing preprocessing, and generating a training sample set and a test sample set; s2, establishing a kernel extreme learning machine prediction model by adopting the generated training sample set; s3, optimizing kernel function parameters and penalty coefficients of the kernel extreme learning machine by adopting a crisscross algorithm in the model training process; andS4, inputting the test sample into a trained kernel extreme learning machine for prediction to obtain a transformer fault diagnosis result. According to the transformer fault diagnosis method, the problem that transformer fault data encoding and kernel extreme learning machine parameter selection are difficult is effectively solved; meanwhile, the local optimization problem of a traditional BP neural network is avoided, the method can be applied to scientific research and engineering application in the related fields of transformers, the recognition speed is high, the recognition rate is high,and the diagnosis precision of transformer faults is greatly improved.

Description

technical field

[0001] The invention relates to the technical field of transformer fault diagnosis, in particular to a transformer fault diagnosis method. Background technique

[0002] The power transformer is the most important power transmission and transformation equipment in the power system, and it is also one of the equipment with the most accidents in the power system. Its operating status directly affects the safety and stability of the system operation. How to ensure the safe operation of transformers has attracted widespread attention from all over the world. Through regular preventive maintenance of power transformers, real-time detection of the actual operation of high-voltage equipment, detection and diagnosis of latent faults or defects, improve the level of diagnosis, achieve targeted maintenance, achieve early prediction of faults, and avoid malignant accidents occurrence has important practical significance. In addition, a lot of data at home and abroad sh...

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