Robust data-driven NLMPC for real-time microgrid management under uncertainties and false data injection attacks.

Elaheh Yaghoubi, Elnaz Yaghoubi, Mehdi Zareian Jahromi, Mohammad Reza Maghami, Mohamed Mazlan

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Elaheh YaghoubiDepartment of Electrical and Electronics Engineering, Istanbul Topkapi University, Istanbul, Turkey.

Elnaz YaghoubiDepartment of Electrical and Electronics Engineering, Istanbul Topkapi University, Istanbul, Turkey.

Mehdi Zareian JahromiDepartment of Electrical and Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan.

Mohammad Reza MaghamiStrategic Research Institute (SRI), Asia Pacific University of Technology and Innovation (APU), 57000, Kuala Lumpur, Malaysia. reza.maghami@apu.edu.my.

Mohamed MazlanFaculty of Artificial Intelligence and Cyber Security (FAIX), Universiti Teknikal Malaysia Melaka, Malacca, Malaysia. mazlan.mohamed@utem.edu.my.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of renewable energy sources in microgrids (MGs) enhances system efficiency but increases vulnerability to cyberattacks such as false data injection (FDI) attacks. This paper presents a robust data-driven nonlinear model predictive control (NLMPC) framework with the integration with Bayesian Neural Networks (BNNs). The BNN offers probabilistic state estimation, allowing uncertainty-aware prediction and early anomaly detection. By integration BNN within the NLMPC, the framework obtains combined detection, mitigation, and control of cyberattacks. Simulation results show that the proposed framework detects FDI attacks within 0.1 s, and stability is restored within 0.4 s. Frequency deviation is reduced by 99.7%, while active and reactive power fluctuation decreases by 85% and 87%, respectively. Moreover, battery storage operation remains within a safe limit. These results validate the effectiveness of the proposed framework for real-time cyber-resilient microgrids control under uncertainty and attack scenarios.

Indexed as

Bayesian neural networkFalse data injection attacksMicrogrid controlNonlinear model predictive controlRenewable energy integrationUncertainty management

Identifiers

PMID41992055

DOI10.1038/s41598-026-48944-y

PMCPMC13392381

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