Ngamroo, Issarachai
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Preferred name
Ngamroo, Issarachai
Alternative Name
Ngamroo, I.
Main Affiliation
Email
issarachai.ng@kmitl.ac.th
4 results
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Item type:Publication, Inertia Assessment From Transient Measurements: Recent Perspective From Japanese WAMS(2022-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Small-Signal Stability Enhancement Through Integration of Distributed Grid-Forming Loads Considering Multi-Agent Collaboration(2025-01-01); ; Mitani, YasunoriThe integration of distributed controllable loads in future islanding microgrids (MGs) is growing. This creates new opportunities to actively shape grid frequency and voltage. As a result, it leads to the development of distributed grid-forming loads (DGFM-Ls). Simultaneously, it is equally crucial to ensure robustness, particularly in preserving small-signal stability amid the multi-agent collaboration. This paper presents a strategy for the small-signal stability enhancement in islanding MGs with DGFM-Ls. The small-signal models of the MG with DGFM-Ls are mathematically developed and analyzed. The multi-agent cooperation is modelled to improve the small-signal stability of the MG with DGFM-Ls. Additionally, uncertainties from multi-agent cooperation, such as partial or complete lack of measured signal observability, are considered. Data quality issues are also taken into account under various conditions. Simulation results are conducted in a MG with a significant penetration of inverter-based resources under various MG operating points and conditions such as topology changes and unavailability of certain agents. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Small-signal analysis of multiple virtual synchronous machines to enhance frequency stability of grid-connected high renewables(2021-04-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligence-Driven Grid-Forming Converter Control for Islanding Microgrids(2025-01-01); ; Mitani, YasunoriIn 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.