Transformer multi-source partial discharge mode identification method based on parallel feature domain

Abstract

The invention discloses a transformer multi-source partial discharge mode identification method based on a parallel feature domain. The method comprises the steps: extracting multi-source partial discharge signals to construct a multi-source partial discharge time domain signal set and a time-frequency domain signal set; combining a plurality of single neural network automatic encoders into two stacked encoders; selecting a sigmoid function as an activation function between the stacked encoder network layers, and obtaining an activation value of the next network layer by using the activation function; adding a regularization item to a loss function for adjusting each layer of network parameters in the stacked encoder; setting an optimization solution method of the loss function as a near-end guide random sub-gradient algorithm; adding softmax as a classification layer of the neural networks; and carrying out parallel training on the multi-source partial discharge time domain signal setand the time-frequency domain signal set by using the stacked encoder, wherein feature matrix data has corresponding labels, and a classification result is compared to finely adjust network parameters. The method has the advantages of high classification precision, strong generalization ability of a deep learning model and the like, and is suitable for occasions such as multi-source partial discharge mode identification of a transformer.

Description

technical field [0001] The invention relates to the field of transformer partial discharge pattern recognition, in particular to a transformer multi-source partial discharge pattern recognition method based on parallel feature domains. Background technique [0002] The traditional multi-source partial discharge identification method is based on the structural feature quantity, but although the artificially constructed features have clear physical meaning, but the dimension is lower; and the deep learning algorithm uses the nonlinear mapping between the hidden layers to integrate the multi-source partial discharge The discharge feature information is abstracted into a multi-dimensional matrix, which covers more feature information and can better represent the original multi-source partial discharge information. [0003] As deep learning is one of the most efficient feature extraction tools, as the number of network layers increases, redundant parameters between network layers...

AI Technical Summary

Problems solved by technology

[0005] ①. The signal separation effect is poor; ②. The artificially extracted signal features are not accurate enough; ③. The neural network algorithm used for pattern recognition has problems such as overfitting during training

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