Prediction method for insulator equivalent salt deposit density and non-soluble deposit density by least squares support vector machine and genetic algorithm

A technology of support vector machine and least squares, applied in the direction of measuring devices, instruments, scientific instruments, etc., can solve the problems of manual measurement of water consumption of ESDD data, troublesome operation, etc., and achieve simplified quadratic programming problems, fast learning speed, The effect of saving computing time

Inactive Publication Date: 2015-04-08
STATE GRID CORP OF CHINA +3
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Problems solved by technology

However, manual measurement of ESDD data is easily affected by water consumption, and the ope

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  • Prediction method for insulator equivalent salt deposit density and non-soluble deposit density by least squares support vector machine and genetic algorithm
  • Prediction method for insulator equivalent salt deposit density and non-soluble deposit density by least squares support vector machine and genetic algorithm
  • Prediction method for insulator equivalent salt deposit density and non-soluble deposit density by least squares support vector machine and genetic algorithm

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Embodiment Construction

[0027] The realization principle of the insulator equivalent salt density and gray scale prediction method based on least squares support vector machine and genetic algorithm in the present invention is as follows: figure 1 As shown, the method mainly includes the following steps:

[0028] Step 1: Design an online leakage current monitoring system for counting changes in time-domain characteristic quantities of leakage current;

[0029] Step 2: Determine the input and output phasors of the least squares support vector machine prediction model and normalize the sample data;

[0030] Step 3: Determine the radial basis kernel function of the least squares support vector machine and the model performance evaluation index;

[0031] Step 4: Optimizing the prediction model of the least squares support vector machine by genetic algorithm.

[0032] Each step is described in further detail below:

[0033] Step 1: Design an online leakage current monitoring system for counting changes...

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Abstract

The invention provides a prediction method for an insulator equivalent salt deposit density ESDD and non-soluble deposit density (NSDD) by a least squares support vector machine and genetic algorithm. The method includes: step 1. firstly designing an insulator leakage current on-line monitoring system for statistical analysis of leakage current time domain feature change; step 2. determining the input and output phasors of a least squares support vector machine prediction model and performing normalization processing on sample data; step 3. determining the radial basis kernel function of the least squares support vector machine and the model performance evaluation index; and step 4. optimizing the least squares support vector machine prediction model by genetic algorithm.

Description

technical field [0001] The invention relates to an insulator equivalent salt density and gray scale prediction method based on a least square support vector machine and a genetic algorithm. Background technique [0002] A large amount of pollution will be deposited on the surface of high-voltage insulators during long-term operation. When the dirt layer is damp, the performance of the insulator will be greatly reduced. In order to prevent large-scale pollution flashover accidents, under the requirement of condition-based maintenance of power transmission and transformation equipment, the power department proposes online monitoring of the pollution status of the insulator surface. At present, most pollution on-line monitoring systems use leakage current data as the core to infer the pollution degree of insulators. Although the leakage current is a dynamic characteristic quantity closely related to pollution flashover, the change of the leakage current value cannot accurately...

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Application Information

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IPC IPC(8): G01N27/00
Inventor 戴亮张少成李胜华李祥明何菲葛乐
Owner STATE GRID CORP OF CHINA
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