Static voltage stability assessment of power systems based on MWMOTE and SSA-KELM

Authors

Songkai LIU, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Jun CAO, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Pan SU, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, ChinaFollow
Kun GAO, Changde Power Supply Branch, State Grid Hunan Electric Power Co., Ltd., Changde 415130, China
Yuheng WU, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Ming WAN, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Di AI, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China

Keywords

unbalanced sample category; static voltage stability assessment; majority weighted minority oversamplingtechnique; sparrow search algorithm; kernel extreme learning machine

Abstract

The static voltage stability assessment method of a power system based on data drive usually has the problem of an unbalanced sample category in the initial data, which makes the performance of the data-driven assessment model greatly affected. Therefore, a static voltage stability assessment method of a power system is proposed based on the majority weighted minority oversampling technique (MWMOTE) and sparrow search algorithm-kernel extreme learning machine (SSA-KELM ) . First, MWMOTE is used to solve the problem of an unbalanced sample category and increase sample diversity. Then, the KELM model parameters are optimized by using SSA, and the static voltage stability assessment model of the power system based on SSA-KELM is constructed. Finally, the validation is carried out on a 10-machine 39-bus system of New England, and the test results show that the proposed method can effectively deal with the problem of unbalanced sample categories with good assessment accuracy and generalization ability.

DOI

10.19781/j.issn.1673-9140.2026.01.002

First Page

13

Last Page

22

Recommended Citation

LIU, Songkai; CAO, Jun; SU, Pan; GAO, Kun; WU, Yuheng; WAN, Ming; and AI, Di (2026) "Static voltage stability assessment of power systems based on MWMOTE and SSA-KELM," Journal of Electric Power Science and Technology: Vol. 41: Iss. 1, Article 2.
DOI: 10.19781/j.issn.1673-9140.2026.01.002
Available at: https://jepst.researchcommons.org/journal/vol41/iss1/2