Power quality disturbance classification method based on sparse automatic coding depth neural network

A technology of power quality disturbance and deep neural network, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of long training time, difficulty in obtaining the deep essential characteristics of fault data, and the accuracy needs to be improved. very robust effect

Inactive Publication Date: 2018-11-20
XIANGTAN UNIV
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Problems solved by technology

ANN has a simple structure, strong problem-solving ability, and can handle noisy data well, but the training time is long and the accuracy needs to be improved
[0004] The above feature extraction methods have achieved good results in the identification of power quality disturbances, bu

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  • Power quality disturbance classification method based on sparse automatic coding depth neural network
  • Power quality disturbance classification method based on sparse automatic coding depth neural network
  • Power quality disturbance classification method based on sparse automatic coding depth neural network

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[0066] Example

[0067] Select 9 kinds of disturbance signals including voltage sag, voltage swell, voltage interruption, oscillation transient, harmonic, voltage gap, voltage spike, harmonic + voltage sag and harmonic + voltage swell, including 2 kinds of composite electric energy The quality disturbance signal, the disturbance model is shown in Table 1.

[0068] Table 1 Power quality disturbance simulation model

[0069]

[0070] In the table, u(g) and sgn(g) are unit step function and sign function, respectively, t 1 , T 2 , T are the start time, end time and signal period of the disturbance respectively, and ω is the rated angular frequency. In order to verify the applicability of the method in this paper, A and h are randomly changed in the simulation experiment. i , K and other parameter values ​​generate multiple sets of power quality disturbance waveforms, and use the method proposed in this article to classify each set of power quality disturbance signals under different pa...

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Abstract

The invention discloses a power quality disturbance classification method based on a sparse automatic coded depth neural network. The method comprises the following steps of preprocessing a power quality disturbance original data set, inputting a preprocessed data sample into a stacked sparse automatic encoder to carry out automatic feature learning to obtain a feature expression, a step of transmitting an extracted feature expression to a softmax classifier for fine adjustment and further adjusting network parameters to obtain a trained classification model, and a step of inputting the a testsample into the classification model for power quality disturbance classification. According to the method, nine power quality disturbance signals including two kinds of composite disturbances can beaccurately identified, and the method has good robustness.

Description

technical field [0001] The invention relates to a method for classifying power quality disturbances, in particular to a method for classifying power quality disturbances based on a sparse automatic encoding deep neural network. Background technique [0002] In recent years, with the rapid development of smart grids and the connection of a large number of new energy power generation devices and impact, nonlinear, and fluctuating loads to the grid, power quality problems in power systems have become increasingly prominent. Various power quality disturbance events have serious impact on industrial production and residents. Lives have had a very serious impact. On the other hand, with the continuous development of science and technology, more and more electrical equipment put forward higher requirements for power quality, and the accurate identification and effective classification of power quality disturbances can provide assistance for the management and governance of power qu...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2136G06F18/241
Inventor 段斌屈相帅杨宇佳晏寅鑫钟颖
Owner XIANGTAN UNIV
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