Transformer winding deformation identification method based on LSTM neural network

Abstract

The invention discloses a transformer winding deformation identification method based on an LSTM neural network. The method comprises the steps of 1) carrying out data cleaning on vibration data collected by a transformer; 2) distinguishing direct current magnetic bias and winding deformation, and calibrating winding deformation data; 3) designing an LSTM neural network; 4) adjusting the parameters of the neural network, and training the adjusted parameters of the neural network; and 5) starting verification by adopting the trained neural network, and performing fault identification on the transformer needing fault identification through the trained neural network. Under the condition that transformer vibration lacks a time domain signal analysis tool, by means of the LSTM long short-term memory neural network technology, time domain signal analysis can be rapidly and effectively carried out; and the method is different from a traditional transformer winding deformation identification method, and after neural network training is completed, the method has the advantages of convenience in neural network establishment, high fault identification efficiency and the like.

Description

technical field [0001] The invention relates to the field of transformers, in particular to a transformer winding deformation fault identification method based on an LSTM neural network. Background technique [0002] In recent years, the manufacturing process and process of transformers have been continuously optimized, but due to the long-term operation of transformers, there will always be damages and latent failures of varying degrees. Under the impact of overload operation and large short-circuit current of the power transformer, the huge electromagnetic force will have a strong impact on the important components such as the iron core and winding of the transformer, resulting in the weakening of the mechanical strength of the components. With the accumulation of damage and the aging and deterioration of the insulation of some components, it is bound to cause some major failures. Therefore, for a long time, in order to ensure the safe operation of the power system and de...

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Problems solved by technology

However, the above research results are all based on the detection of vibration signals of one or several transformers in the laboratory or operation site, and the data are most scarce.

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