Online Prediction and Correction of Static Voltage Stability Index Based on Extreme Gradient Boosting Algorithm

Huiling Qin, Shuang Li (), Juncheng Zhang, Zhi Rao, Chengyu He, Zhijun Chen and Bo Li
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Huiling Qin: Guangxi Power Grid Corporation, Nanning 530004, China
Shuang Li: China Southern Power Grid Co., Ltd., Guangzhou 510530, China
Juncheng Zhang: Guangxi Power Grid Corporation, Nanning 530004, China
Zhi Rao: China Southern Power Grid Co., Ltd., Guangzhou 510530, China
Chengyu He: Guangxi Power Grid Corporation, Nanning 530004, China
Zhijun Chen: Guangxi Power Grid Corporation, Nanning 530004, China
Bo Li: School of Electrical Engineering, Guangxi University, Nanning 530004, China

Energies, 2024, vol. 17, issue 22, 1-14

Abstract: With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive control becomes crucial. This article proposes a real-time prediction and correction method based on the extreme gradient boosting (XGBoost) algorithm. The XGBoost algorithm is utilized to evaluate the real-time stability of grid static voltage, with the voltage stability L-index as the prediction target. A correction model is established with the objective of minimizing correction costs while considering the operational constraints of the grid. When the L-index exceeds the warning value, the XGBoost algorithm can obtain the importance of each feature of the system and calculate the sensitivity approximation of highly important characteristics. The model corrects these characteristics to maintain the system’s operation within a reasonably secure range. The methodology is demonstrated using the IEEE-14 and IEEE-118 systems. The results show that the XGBoost algorithm has higher prediction accuracy and computational efficiency in assessing the static voltage stability of the power grid. It is also shown that the proposed approach has the potential to greatly improve the operational dependability of the power grid.

Keywords: static voltage stability; preventive control; real-time prediction; extreme gradient boosting (XGBoost) algorithm; sensitivity approximation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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