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Regular Paper | Open Access

Preventive-corrective Control for Static Voltage Stability Under Multiple N − 1 Contingencies Considering Wind Power Uncertainty

Yuerong Yang1Shunjiang Lin1 ( )Qiong Wang1Mingbo Liu1Qifeng Li2
School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816 USA
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Abstract

An optimal preventive-corrective control model for static voltage stability under multiple N 1 contingencies considering the wind power uncertainty is established in this paper. The objective is to minimize the control variable adjustment cost including the load shedding cost of each contingency. The chance constraints of the static voltage stability margins (SVSMs) in the normal operation state and after each N 1 contingency are included. The approximate functions between the probability density functions (PDFs) of SVSMs and load shedding quantity with respect to preventive control variables are obtained to transform the expectation of load shedding quantity and the SVSM chance constraints into deterministic expressions. An approximate sequential convex quadratically constrained quadratic programming iteration method is proposed to solve the optimal control model. In each iteration, the approximate expressions and range are determined by the generated data samples. Moreover, a fast approximation calculation method of second-order matrices is proposed. By the naive Bayes classifier, the most severe N 1 contingencies are selected to replace all the contingencies to be added to the optimization model to improve the computational efficiency. Case studies on the IEEE-39 bus system and an actual provincial power grid demonstrate the effectiveness and efficiency of the proposed method.

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CSEE Journal of Power and Energy Systems
Pages 1466-1480

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Cite this article:
Yang Y, Lin S, Wang Q, et al. Preventive-corrective Control for Static Voltage Stability Under Multiple N − 1 Contingencies Considering Wind Power Uncertainty. CSEE Journal of Power and Energy Systems, 2025, 11(4): 1466-1480. https://doi.org/10.17775/CSEEJPES.2023.00310

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Received: 18 January 2023
Revised: 17 March 2023
Accepted: 31 May 2023
Published: 03 May 2024
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).