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

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...

AI Technical Summary

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 iterative learning algorithms, it is easy to fall into the local extremum problem, it is also sensitive to the initial value, and the iterative calculation process takes a long time

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