Intelligence-Driven Grid-Forming Converter Control for Islanding Microgrids

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dc.contributor.authorNgamroo, Issarachai
dc.contributor.authorSurinkaew, Tossaporn
dc.contributor.authorMitani, Yasunori
dc.date.accessioned2026-08-06T10:49:34Z
dc.date.available2026-08-06T10:49:34Z
dc.date.issued2025-01-01
dc.description.abstractIn modern microgrids (MGs) with high penetration of distributed energy resources (DERs), system reconfiguration occurs more frequently and becomes a significant issue. Fixed-parameter controllers may not handle these tasks effectively, as they lack the ability to adapt to the dynamic conditions in such environments. This paper proposes an intelligence-driven grid-forming (GFM) converter control method for islanding MGs using a robustness-guided neural network (RNN). To enhance the adaptability of the proposed method, traditional proportional-integral controllers in the GFM primary control loops are entirely replaced by the RNN. The RNN is trained by a robustness-guided strategy to replicate their robust behaviors. All the training stages are purely data-driven methods, which means that no system parameters are required for the controller design. Consequently, the proposed method is an intelligence-driven modelless GFM converter control. Compared with traditional methods, the simulation results in all testing scenarios show the clear benefits of the proposed method. The proposed method reduces overshoots by more than 71.24%, which keeps all damping ratios within the stable region and provides faster stabilization. In comparison to traditional methods, at the highest probability, the proposed method improves damping by over 14.7% and reduces the rates of change of frequency and voltage by over 59.97%. Additionally, the proposed method effectively suppresses the interactions between state variables caused by inverter-based resources, with frequencies ranging from 1.0 Hz to 1.422 Hz. Consequently, these frequencies contribute less than 19.79% To the observed transient responses.
dc.identifier.citationJournal of Modern Power Systems and Clean Energy, 13(4), 1310-1322, 2025
dc.identifier.doi10.35833/MPCE.2024.001157
dc.identifier.issn21965625
dc.identifier.other2-s2.0-105012378333
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16560
dc.sourceJournal of Modern Power Systems and Clean Energy
dc.subjectDistributed energy resource
dc.subjectgrid-forming converter
dc.subjectintelligence-driven control
dc.subjectmicrogrid
dc.subjectneural network
dc.subjectstability
dc.titleIntelligence-Driven Grid-Forming Converter Control for Islanding Microgrids
dc.typeArticle

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