| 摘要: | 本論文提出一種儲能系統與基於太陽能光電系統之配電型靜態同步補償器(Photovoltaic Distribution Static Synchronous Compensator, PV-DSTATCOM)組成的下垂控制微電網，由於分散式可再生能源發電系統(DGs)快速發展、感性負載廣泛使用及負載突變，造成電力品質上的問題，例如電流不平衡、電流諧波、功率因數落後等，因此，提出一種新型的PV-DSTATCOM來改善電力品質的問題。
此外，為了有效改善在負載變化時的虛功率補償暫態響應，首次提出了具有線上訓練能力的派翠勒壤得模糊神經網路(Petri Legendre Fuzzy Neural Network, PLFNN)用於取代傳統的比例積分（Proportional-Integral, PI）控制器，並且本論文詳細推導提出的PLFNN之網路架構和線上學習策略。最後，利用實作及電腦模擬結果驗證DSTATCOM使用所提出的PLFNN控制器於下垂控制微電網中改善電流不平衡、降低電流總諧波失真(Total Harmonic Distortion, THD)、功率因數校正(Power Factor, PF)和改善暫態響應的有效性。
;A droop controlled microgrid composed of a battery energy storage system (BESS) and a photovoltaic based distribution static synchronous compensator (PV-DSTATCOM) is developed in this study for the power quality improvement. Owing to the high penetration rate of the renewable energy source-based distributed generators (DGs), extensive usage of the inductive loads, and unexpected load change, the power quality issues, including unbalanced currents, current harmonics, and lagging power factor (PF), have become severe challenges in microgrid. Consequently, a novel control algorithm of PV-DSTATCOM is firstly proposed to overcome the power quality issues. The PV-DSTATCOM owns the droop characteristic and the ability to compensate the reactive power for power quality improvement. Moreover, to effectively improve the transient response of the reactive power compensation and the performance of the PV-DSTATCOM during load variations, an online trained Petri Legendre fuzzy neural network (PLFNN) controller is firstly proposed to replace the conventional proportional-integral (PI) controller. Furthermore, the network structure and the online learning algorithm of the proposed PLFNN are detailedly derived. Finally, the effectiveness of the PV-DSTATCOM using the proposed PLFNN controller in the microgrid to reduce the total harmonic distortion (THD) of the current, correct the PF and compensate the three-phase unbalanced currents is verified by simulation and experimentation. |
