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

Author

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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)

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.

Suggested Citation

  • Huiling Qin & Shuang Li & Juncheng Zhang & Zhi Rao & Chengyu He & Zhijun Chen & Bo Li, 2024. "Online Prediction and Correction of Static Voltage Stability Index Based on Extreme Gradient Boosting Algorithm," Energies, MDPI, vol. 17(22), pages 1-14, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5710-:d:1521525
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    References listed on IDEAS

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    1. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
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