Transient stability assessment input characteristic validity analysis method based on improved Sammon mapping

An analysis method and effective technology, applied in the field of input feature validity analysis based on improved Sammon mapping for temporary stability assessment, which can solve the problems of sensitive initial value and long time-consuming iterative calculation process.

Active Publication Date: 2016-10-26
CHINA ELECTRIC POWER RES INST +2
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Problems solved by technology

[0004] However, the traditional sammon mapping algorithm directly uses optimization methods such as quasi-Newton method or gradient descent method to solve
Like other iterati

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  • Transient stability assessment input characteristic validity analysis method based on improved Sammon mapping
  • Transient stability assessment input characteristic validity analysis method based on improved Sammon mapping
  • Transient stability assessment input characteristic validity analysis method based on improved Sammon mapping

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[0055] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0057] A kind of transient stability assessment input feature validity analysis method based on improved Sammon mapping provided by the present invention, such as figure 1 shown, including:

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Abstract

The present invention relates to a transient stability assessment input characteristic validity analysis method based on improved Sammon mapping. The method comprises removing redundant data in power grid operation sample data by using a fuzzy C-means clustering algorithm; obtaining three-dimensional data having the maximum contribution rate in the power grid operation sample data by using a principal component analysis method as initial values of a low-dimensional space mapping point coordinate vector of the power grid operation sample data; correcting the initial values of the low-dimensional space mapping point coordinate vector of the power grid operation sample data by using an iteration correction method, and obtaining the low-dimensional space mapping point coordinate vector of the power grid operation sample data; and judging validity of the power grid operation sample data according to the low-dimensional space mapping point coordinate vector of the power grid operation sample data. According to the method, when transient stability assessment is performed on a power system, the initial values are provided for iteration by using the principal component analysis method to stabilize a mapping result, low-dimensional space mapping point coordinates are solved by using the iteration correction method, and the solving speed is accelerated.

Description

technical field [0001] The invention relates to the technical field of power system safety and stability analysis, in particular to a method for analyzing the validity of input features of transient stability evaluation based on improved Sammon mapping. Background technique [0002] In the power system transient stability assessment (TSA) method based on machine learning technology, the stability judgment result can be treated as a classification problem. That is, by selecting a set of appropriate classification features to describe the state of the system, a high-dimensional input space is established, and then a suitable classification method is used to classify the samples. The quality of classification results is not only related to the performance of the adopted classification method, but also to the selected feature subset, because the expressive power of the selected input features determines the separability of the input space. If the selected input features are inv...

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00Y04S10/50
Inventor 张春于之虹张军鲁广明杨超平戴红阳张爽田芳高峰李岩松田蓓
Owner CHINA ELECTRIC POWER RES INST
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