Sparse automatic encoder optimized neural network-based power transmission line fault identification method

A sparse automatic encoder and transmission line technology, applied in the field of power transmission and distribution, can solve the problems of limited data samples, less label data, and cumbersome acquisition process, and achieve the effect of improving the accuracy of fault identification.

Inactive Publication Date: 2019-06-18
珠海妙微科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Neural network has strong learning and generalization capabilities, so it is widely used in various classifiers. However, the premise of supporting this method is to extract appropriate feature parameters and require a large number of data samples to realize net

Method used

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  • Sparse automatic encoder optimized neural network-based power transmission line fault identification method
  • Sparse automatic encoder optimized neural network-based power transmission line fault identification method
  • Sparse automatic encoder optimized neural network-based power transmission line fault identification method

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Embodiment 1

[0042] Such as figure 1 As shown, the embodiment of the present invention provides a transmission line fault identification method with a neural network optimized by a sparse autoencoder, including:

[0043] S1 data acquisition, collecting the fault current traveling wave generated on the transmission line after the transmission line fails;

[0044] S2 extracting fault waveform feature quantities: extracting feature quantities from the transmission line fault current traveling wave data collected in the step S1, including wavelength time; pre-flashover polarity; fundamental wave content; high and low frequency band energy ratio four feature quantities;

[0045] The S3 sparse autoencoder is trained using the stochastic gradient descent algorithm to obtain the sparse expression of the inherent feature information in the four kinds of feature quantities in the step S2, and save the sparse autoencoder weight W and bias b after the training ends;

[0046] S4 initializes the input ...

Embodiment 2

[0070] An embodiment of the present invention provides a method for identifying a fault type of a transmission line based on a neural network optimized by a sparse autoencoder, comprising the following steps:

[0071] 11. Data collection, collecting the fault current traveling wave generated on the wire after the transmission line fails, the fault current traveling wave data is the current traveling wave of 0.5ms before and after the fault occurs.

[0072] 12. Extracting fault waveform feature quantities: performing time domain and frequency domain analysis on the transmission line fault current traveling wave collected in step 11, the specific process is as follows:

[0073] 121 analysis figure 2 , image 3 According to the current traveling wave path of counterattack and shielding faults in the transmission line, in the case of counterattack faults, before the insulator flashover, there is an induced current of reverse polarity on the transmission wire, but it does not exi...

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Abstract

The embodiments of the invention disclose a sparse automatic encoder optimized neural network-based power transmission line fault identification method. The method includes the following steps that: S1, data acquisition is performed: fault current traveling waves generated on a power transmission line after the failure of the power transmission line are acquired; S2, a fault waveform feature quantity is extracted: the feature quantity of the fault current traveling wave data of the power transmission line which are acquired in the step 1 is extracted; S3, a sparse automatic encoder is trainedwith a random gradient descent algorithm, so that the sparseness expression of intrinsic feature information in the feature quantity in the step S2, the weight W and offset b of the sparse automatic encoder which are obtained after the training is finished are saved; S4, the input layer and hidden layer of a neural network are initialized with the weight W and the offset b of the sparse automaticencoder in the step S3, a forward iterative algorithm and a reverse iterative algorithm are adopted to train the neural network; and S5, the optimized neural network and a BP neural network are adopted to identify the fault type of the transmission line under the condition of different quantities of training samples.

Description

technical field [0001] The invention relates to the field of power transmission and distribution, in particular to a transmission line fault identification method based on a neural network optimized by a sparse automatic encoder. Background technique [0002] Power system transmission lines have long distances, wide geographical distribution, and complex environments. They are often affected by factors such as lightning, animals and plants, ice and snow weather, etc., causing transmission line faults to trip. The types of transmission line faults can be divided into lightning strike faults and non-lightning strike faults, and the protective measures for different faults are different. Therefore, it is particularly important to accurately identify the fault types of transmission lines. [0003] Transmission line fault type identification is mainly divided into two aspects: fault feature extraction and fault type identification. The characteristic quantities of transmission l...

Claims

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Application Information

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IPC IPC(8): G01R31/08
Inventor 林顺富于俊苏徐美金顾春艳高健飞畅国刚吕乔榕许亮峰
Owner 珠海妙微科技有限公司
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