本論文提出一種具有配電型靜態同步補償器(Distribution Static Compensator, DSTATCOM）的下垂控制微電網，以改善微電網之電力品質。由於虛功率/電壓Q-V下垂特性，以及不平衡、線性和非線性負載的存在，造成微電網於孤島模式下具有嚴重的電力品質問題，包括電壓降、不平衡電流、落後功率因數（Power Factor, PF）和電流諧波等。此外，由於負載變化時若DSTATCOM中的直流鏈電壓之變化控制不宜，將造成DSTATCOM改善電力品質的性能嚴重退化。因此，為了有效改善下垂控制微電網的電力品質和負載變化下DSTATCOM中直流鏈電壓的暫態響應，本文提出了一種線上訓練的多項式派翠模糊神經網路（Polynomial Petri Fuzzy Neural Network, PPFNN）控制器作為直流鏈電壓控制器，以用於取代DSTATCOM中的傳統比例積分(Proportional-Integral, PI)控制器，並且本文詳細推導所提出的PPFNN之網路架構和線上學習策略。最後，本文利用模擬及實驗以驗證DSTATCOM 使用所提出的PPFNN控制器於下垂控制微電網中改善不平衡電流、降低電流總諧波失真(Total Harmonic Distortion, THD)與虛功率補償的有效性。;A droop controlled microgrid with distribution static compensator (DSTATCOM) is developed to improve the power quality in this thesis. Due to the reactive power/voltage Q-V droop characteristic and the existence of the unbalanced, linear inductive and nonlinear loads, the power quality problems including the voltage drop, unbalanced currents, lagging power factor (PF) and current harmonics are very serious in islanded microgrid. Moreover, if the design of the control of the DC-link voltage in the DSTATCOM is not suitable under load variation, the performance of the DSTATCOM for power quality improvement is seriously degenerated. Hence, to effectively improve the power quality of the droop controlled microgrid and the transient response of the DC-link voltage in the DSTATCOM under load variation, an online trained polynomial Petri fuzzy neural network (PPFNN) controller is proposed as the DC-link voltage controller to supersede the conventional proportional-integral (PI) controller. Furthermore, the network structure and the online learning strategy of the proposed PPFNN are detailedly derived. Finally, the effectiveness of the DSTATCOM using the proposed PPFNN controller in droop controlled microgrid to improve the unbalanced current, the total harmonic distortion (THD) reduction of the current and to compensate the reactive power is verified by using simulation and experimentation.
