Open Access

ARTICLE

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

Zhibo Yang, Xiaohan Huang, Bingdong Wang, Bin Hu, Zhenyong Zhang^*

State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

* Corresponding Author: Zhenyong Zhang. Email:

Computers, Materials & Continua 2024, 80(2), 3001-3019. https://doi.org/10.32604/cmc.2024.053369

Received 29 April 2024; Accepted 16 July 2024; Issue published 15 August 2024

Abstract

With the widespread use of machine learning (ML) technology, the operational efficiency and responsiveness of power grids have been significantly enhanced, allowing smart grids to achieve high levels of automation and intelligence. However, tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks, making it urgent to enhance their robustness. To address this, we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles. Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws, ensuring training data accurately reflects possible attack scenarios while adhering to physical rules. In our experiments, the proposed method increased robustness against adversarial attacks by 100% when applied to real grid data under physical constraints. These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

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Keywords

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