An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction

doi:10.3969/j.issn.1008-0198.2022.05.004

Hunan Electric Power ›› 2022, Vol. 42 ›› Issue (5): 22-28.doi: 10.3969/j.issn.1008-0198.2022.05.004

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An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction

LI Shuaihu¹, ZHAO Xiang ¹, JIANG Yunchen²

1. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410014, China;
2. State Grid Changsha Power Supply Company, Changsha 410011, China

Received:2022-07-15 Revised:2022-07-29 Online:2022-10-25 Published:2022-11-16

Abstract

Abstract: In this paper, an online voltage stability evaluation method based on particle swarm optimization support vector regression(PSO-SVR) for large power grid is proposed. The traditional voltage stability evaluation method based on deep neural networks(DNN) model is improved to the SVR model optimized by PSO to predict the impedance modulus margin index.This method takes advantage of SVR model′s advantages of strong learning ability and low generalization error rate, and it can also learn the features of samples well in the case of small samples.In order to overcome SVR model is very sensitive to parameter adjustment and function selection.The proposed method uses PSO algorithm to optimize the hyper-parameters of SVR model, which can make the SVR model better learn the nonlinear relationship between power grid operation data and impedance modulus margin value.Finally, the proposed method is validated in IEEE 118 node system, and the accuracy of the proposed method is higher than that of DNN model-based evaluation method.

Key words: power system, static voltage stability, impedance modulus margin, particle swarm optimization, support vector regression

CLC Number:

TM712.2

LI Shuaihu, ZHAO Xiang , JIANG Yunchen. An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction[J]. Hunan Electric Power, 2022, 42(5): 22-28.

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An On-Line Voltage Stability Assessment Method based on Particle Swarm Optimization Support Vector Regression Prediction

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