Transient power disturbance identification method based on S conversion and improved SVM algorithm

A recognition method and algorithm technology, applied in character and pattern recognition, computing, computer parts, etc., can solve the problem of low feasibility of classification

Inactive Publication Date: 2016-03-30
STATE GRID CORP OF CHINA +3
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However, when the prediction vector is dense,

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  • Transient power disturbance identification method based on S conversion and improved SVM algorithm
  • Transient power disturbance identification method based on S conversion and improved SVM algorithm
  • Transient power disturbance identification method based on S conversion and improved SVM algorithm

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[0027] In order to make the objects and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention

[0028] Such as figure 1 As shown, the embodiment of the present invention provides a transient power disturbance identification method based on S-transform and improved SVM algorithm, which includes the following steps:

[0029] 1) Process the disturbance signal using the improved S-transform:

[0030] The key to improving the S transform is to add the adjustment factor λ to the Gaussian window M , according to the frequency distribution of the sample signal, speed up or slow down the speed of the Gaussian window width changing with frequency. The calculation formula of the improved S-transform is:

[0031] s ...

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Abstract

The invention discloses a transient power disturbance identification method based on S conversion and an improved SVM algorithm. The method comprises the following steps: (1), carrying out processing on a disturbance signal based on improved S conversion; (2), extracting a disturbance signal characteristic; and (3) designing an SVM classifier based on a semi-supervised learning algorithm to classify samples. Compared with the previous power quality disturbance classification method, the provided method has beneficial effects: on the premise that the identification accuracy of the SVM algorithm is guaranteed, the improved semi-supervised learning algorithm is introduced into the sample with low reliability in the SVM algorithm, so that the identification accuracy of the disturbance signal can be improved; and advantages of good scientific and reasonable performances, high adaptability, and great promotional value and the like are realized.

Description

technical field [0001] The invention is a transient power disturbance identification method based on S transformation and improved SVM algorithm, which is applied to automatic classification and positioning of power quality transient disturbances, online monitoring and evaluation of equipment status, and power quality management. Background technique [0002] The existence of non-linear loads, impact loads and single-phase loads has seriously polluted the environment of the power grid, and the resulting power quality problems have also attracted people's attention. The automatic classification technology of power quality transient disturbances is an important basis for power quality analysis and control, and is of great significance to transient management, power electronic equipment status monitoring, and disturbance source location. In order to improve people's living standards and ensure normal industrial production, it is necessary to ensure that the power system can pro...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 李春来门洪刘佳张晋宝
Owner STATE GRID CORP OF CHINA
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