Partial discharge network training method and device for phase discrimination of power equipment

Abstract

The invention discloses a partial discharge network training method and device for phase discrimination of power equipment. The partial discharge network training method comprises the steps: collecting a partial discharge spectrum of phase discrimination of a partial discharge measurement signal, forming an original detection sample set of the partial discharge of the power equipment, and carryingout preprocessing on an original detection sample; adopting a whitening mechanism to reprocess the preprocessed detection samples, and inputting a part of the detection samples as training data intoa neural network input layer; training the neural network, and optimizing the output of the neural network according to the loss function of the neural network; and predicting the partial discharge fault classification of the power equipment by using the rest of the detection samples as test data. According to the partial discharge network training method, firstly, a sample is subjected to whitening mechanism processing, so that the dimension of the sample is reduced, redundant data of the sample are removed, and the overfitting problem of the neural network in training is prevented; and the loss function of the neural network is improved, and the training accuracy of the power equipment partial discharge defect diagnosis neural network is improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of power equipment, in particular to a partial discharge network training method and device for phase resolution of power equipment. Background technique [0002] In modern power systems, partial discharge is an effective method for diagnosing power equipment faults. However, due to the complexity of partial discharge data, traditional data classification methods are difficult to obtain high diagnostic accuracy. At present, the neural network algorithm can realize the effective discrimination of partial discharge faults to a certain extent, but because the redundant data of the PD training samples is too much and is three-dimensional data, the neural network model has over-fitting phenomenon, and the partial discharge fault The discrimination accuracy is low. Contents of the invention [0003] Purpose of the invention: In order to overcome the deficiencies of the prior art, the present ...

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Problems solved by technology

[0002] In modern power systems, partial discharge is an effective method for diagnosing power equipment faults, but due to the complexity of partial discharge data, traditional data classification methods are difficult to achieve high diagnostic accuracy

At present, the neural network algorithm can realize the effective discrimination of partial discharge faults to a certain extent, but because the redundant data of the PD training samples is too much and is three-dimensional data, the neural network model has over-fitting phenomenon, and the partial discharge fault low discrimination accuracy

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