本論文提出一種併網型微電網結合虛擬慣量利用主、從控制法則克服一般傳統分散式電源基於功率開關元件變流器之缺點，例如缺乏傳統發電機的慣量特性與獨立電網形成能力。本論文的微電網系統控制方法採用主、從控制法則，並且由一儲能系統、太陽光發電系統與一個三相可變電阻負載所組成。其中以儲能系統當作微電網控制主機(Master)，而太陽光發電系統則定位為從屬(Slave)部分。此外，為了改善儲能系統在併網模式時的虛功控制以及微電網在併網與孤島模式之間切換的暫態響應，本論文提出一線上訓練的遞迴式機率小波模糊類神經網路(Recurrent Probabilistic Wavelet Fuzzy Neural Network, RPWFNN)取代傳統比例積分控制器。此外，當微電網在孤島模式運轉時，負載的變化將會造成微電網電壓嚴重地波動，因此本文所提出的RPWFNN也可用來改善因負載變動所造成的微電網電壓波動。本文將詳細介紹RPWFNN的網路架構與線上學習法則。最後，以實驗結果驗證使用RPWFNN之結合虛擬慣量併網型微電網在不同操作模式下之有效性與可行性。;A microgrid with virtual inertia using master-slave control is proposed in this study to overcome the drawbacks of traditional inverter-based distributed generators such as lack of inertia and without grid-forming capability. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system and a varying resistive three-phase load. The storage system and PV system are regarded as the master unit and the slave unit respectively in the microgrid. Moreover, in order to improve the reactive power control in grid-connected mode and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent probabilistic wavelet fuzzy neural network (RPWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Furthermore, when the microgrid is operated in islanding mode, the load variation will have serious influence on the voltage control of the microgrid. Thus, the RPWFNN control is also proposed to improve the transient and steady-state responses of voltage control in the microgrid. Finally, according to some experimental results, the excellent control performance of the microgrid with virtual inertia using the proposed intelligent controller can be achieved.
