Research on practical power system stability analysis algorithm based on modified SVM

Kaiyuan Hou,Shuang Wu,Le Zheng,Haiming Wang,Qiang Zhang,Guanghui Shao,Wei Hu

doi:10.1186/s41601-018-0086-0

Abstract

Stable and safe operation of power grids is an important guarantee for economy development. Support Vector Machine (SVM) based stability analysis method is a significant method started in the last century. However, the SVM method has several drawbacks, e.g. low accuracy around the hyperplane and heavy computational burden when dealing with large amount of data. To tackle the above problems of the SVM model, the algorithm proposed in this paper is optimized from three aspects. Firstly, the gray area of the SVM model is judged by the probability output and the corresponding samples are processed. Therefore the clustering of the samples in the gray area is improved. The problem of low accuracy in the training of the SVM model in the gray area is improved, while the size of the sample is reduced and the efficiency is improved. Finally, by adjusting the model of the penalty factor in the SVM model after the clustering of the samples, the number of samples with unstable states being misjudged as stable is reduced. Test results on the IEEE 118-bus test system verify the proposed method.

Highlights

In China’s current social development, the scale of the power grid and the demand for electrical energy have been increasing continuously
This paper proposes a two-segment support vector machine (SVM) algorithm, mainly aimed at improving the classification accuracy of the analysis of the security regions in the power grid, dealing with the grey space of the SVM model, and reducing the damage of the unstable samples to the power system caused by misjudgment of them being stable ones
3.1 Power system security regions analysis method based on two-segment SVM model The proposed algorithm is improved for single or multiple SVM by improving the accuracy of single SVM classifier

Summary

Introduction

In China’s current social development, the scale of the power grid and the demand for electrical energy have been increasing continuously. By searching for an optimal hyperplane in the sample space, this method divides the samples into two categories and has the advantages of simple model and good classification effect. It has been widely studied and applied by researchers [12,13,14]. The final stability classification results are obtained through voting and the errors can be reduced This algorithm increases the computational complexity of the model

Methods

Results

Discussion

Conclusion