本論文提出利用前饋式神經網路及自適應網路模糊推論系統控制演算法，使用於太陽能光伏系統中的最大功率點追蹤中，俾達成太陽能光伏系統輸出功率的最大化。本論文之研究系統架構一是由太陽能光伏系統、直流對直流升壓轉換器連接負載所組成，研究系統架構二則是由架構一之太陽能光伏發電系統、全釩氧化還原液流電池經直流鏈連接直流負載所組成。本論文採用MATLAB/Simulink環境設計太陽能光伏系統中的最大功率點追蹤，並且利用實際的輻照度及溫度訓練前饋式神經網路及自適應網路模糊推論系統。本論文所提出之前饋式神經網路及自適應網路模糊推論系統演算法，亦與傳統之擾動觀察法在太陽能光伏系統之性能上做比較。由模擬結果分析得知，本論文所提出之方法於太陽能光伏系統之不同性能上均有所改善。

In this thesis, both feedforward neural network (FFNN) and adaptive network-based fuzzy inference system (ANFIS) are proposed to employ in the maximum power point tracking (MPPT) of a photovoltaic (PV) system to maximize its output power. The first system configuration in this thesis composes of a PV system and a DC-DC boost converter connected to the load while the second system configuration consists of the mentioned PV system and a vanadium redox flow battery (VRFB) connected to the DC load through the DC link. The MPPT of the studied PV system under MATLAB/Simulink is designed using practical irradiance and temperature data to train FFNN and ANFIS. The performance of the FFNN and ANFIS is also compared with the one using traditional perturbation and observation (P&O) method. From the simulation results, the performances of the studied PV systems using the proposed FFNN and ANFIS in the thesis have been improved.

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