本研究針對電網形成型變流器於併網運轉與離網運轉模式下之動態響應特性進行模擬分析，提出一套結合輔助分類生成式對抗網路與深度神經網路之人工智能模型，以作為自適應虛擬慣量及自適應阻尼參數之預測控制，有助於提升系統於不同運轉情境的頻率穩定性。本文在核心控制策略上，採用虛擬同步發電機控制技術，使變流器具備慣量與阻尼特性，並透過人工智能模型以即時調整控制參數。而為驗證本文所提方法之可行性，本文模擬不同系統擾動情境，包括電網故障與負載變動，藉以觀察本文所提方法對於虛擬慣量及阻尼參數之調控能力與響應效果。茲由模擬結果顯示，當變流器運轉於併網模式時，本文方法能夠提供電壓支撐及有效抑制頻率振盪；而於離網模式下，則可穩定支撐微電網運轉，以維繫系統面對負載變化時之頻率穩定。本文研究成果可應用於電網形成型變流器之運轉控制，同時可作為提升電網運轉韌性時之施行參考。

This study conducts simulation analysis on the dynamic response characteristics of grid-forming inverter under both grid-connected and islanded operating modes. An artificial intelligence model that integrates an auxiliary classifier generative adversarial network with a deep neural network, serving as a predictive control method for adaptive virtual inertia and adaptive damping parameters, thereby enhancing frequency stability under various operating scenarios. The core control strategy of the proposed method adopts the virtual synchronous generator technique for the inverter to emulate inertia and damping characteristics. The proposed AI model allows real-time adjustment of control parameters in response to system operating conditions. To validate the feasibility of the proposed approach, this study simulates different disturbance scenarios including grid faults and load variations, where the model’s prediction accuracy and control performance is assessed with respect to virtual inertia and damping. Simulation results show that under grid-connected mode, the proposed method can provide voltage support and suppress frequency oscillations effectively. Then in the islanded mode, the method can stably support microgrid operation and maintain frequency stability under load changes. The findings of this study can be applied to the operational control of grid-forming inverter and serve as a reference for enhancing the resilience of power system operations.

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