本論文中提出了應用於太陽能系統之非對稱型模糊控制最大功率追蹤法。本論文在輸入歸屬函數的設計上提出了二種設計方法，可以提升非對稱模糊控制最大功率追蹤法的性能。第一種設計方法是利用標準測試條件下之太陽能電池的功率-電壓曲線特性快速決定輸入歸屬函數設定值。第二種設計方法則是使用粒群演算法獲得最佳化輸入歸屬函數設定值。因為粒群演算法必須依據目標函數做最佳化，因此，本文以實際的太陽能發電系統為基準，提出設計目標函數的方法。本文使用低成本微處理器dsPIC33FJ16GS502實現非對稱型模糊控制最大功率追蹤法，並完成一個300 W原型電路進行實驗以與模擬相互驗證所提出的最大功率追蹤法是正確以及有效的。相較於對稱型模糊控制最大功率追蹤法，所提方法在標準測試條件下追蹤速度及追蹤精確度上分別提升了25.8%及0.93%。此外，對稱型模糊控制最大功率追蹤法在低照度時會存在無法追到最大功率點的問題，而本文提出之非對稱型模糊控制最大功率追蹤法可以有效的處理此一問題。第一種歸屬函數設計方法的優點為簡單而且容易應用。第二種歸屬函數設計方法使用粒群演算法獲得最佳化輸入歸屬函數設定值。相較於第一種設計方法，此設定值在標準測試條件下追蹤速度及追蹤精確度可以更進一步提升0.88%及0.98%。因此可以證明粒群演算法可以成功地應用於獲得最佳化的歸屬函數設定值。此外，粒群最佳化非對稱型模糊控制最大功率追蹤法相較於其他的最大功率追蹤法具有最高的適應值。

In this dissertation, an asymmetrical fuzzy-logic-control (FLC) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target a cost function and optimization, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. The proposed asymmetrical FLC-based MPPT algorithm is implemented using a low cost digital signal controller dsPIC33FJ16GS502. To validate the correctness and the effectiveness of the proposed method, a 300 W prototyping circuit is built and simulations as well as experiments are carried out accordingly. Compared with the symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are improved by 25.8% and 0.93% under STC, respectively. Moreover, since the symmetrical FLC-based MPPT method fails to track the real MPP when irradiance level is low, the proposed asymmetrical FLC-based MPPT method can successfully deal with this problem. The advantages of the first MF design method are that it is simple and easy to adopt. The second MF design method applies the PSO technique to obtain the optimized input MF setting values. Compared with the first design method, the transient time and the MPP tracking accuracy can further be improved by 0.88% and 0.98%, respectively. This proves that PSO can be successfully applied to obtain the optimized MF setting values. In addition, the PSO optimized asymmetrical FLC-based MPPT method has the highest fitness value compared with other implemented methods.

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