燃料電池與太陽能皆為乾淨零排放的能源技術，在未來的電能供應角色上相當被重視，因此許多先進國家投入鉅資開發，來降低對石化能源及核能的依賴。這兩種能源系統皆須搭配直流/直流轉換器與良好控制器來使它發揮最大的效益。
本論文主旨是以模糊控制器為控制架構，於燃料電池的混合供電系統中建立模糊滑動平面控制演算法，根據不同負載以及在操作中負載切換時，此演算法可達到穩定輸出電壓。經由數位訊號處理器(DSP)實驗結果可以證實模糊滑動平面控制在規則庫縮減為傳統模糊控制器的平方根倍，故實現於場域可程式邏輯閘陣列(FPGA)時，可降低在合成邏輯閘之數目。從FPGA實際量測的結果，控制演算法已成功導入此系統且也達到穩定輸出電壓值。之後將該演算法在數位工作站進行模擬與測試，根據暫存器傳輸級模擬結果與我們的設計相符。
太陽能供電系統中，最大功率追蹤演算法的效能會決定整個太陽能系統之輸出效率。其中，擾動觀察法與增量電導法很常被使用在太陽能系統，但是這兩種演算法追蹤速度比較慢以及會在最大功率點震盪而導致功率損耗與系統不穩定。因此第一型模糊控制被應用於增加追蹤速度以及減少穩態震盪的情況，但無法抑制雜訊的干擾。故本論文最大功率追蹤演算法採用區間第二型模糊控制，此演算法追蹤速度較快以及能夠抑制雜訊的干擾。經由實驗結果，於最大功率追蹤部分本文在固定日射量的轉換效率可達98.9%，動態情況下可達到99.2%，皆較傳統演算法為佳，因此使整體太陽能系統的轉換效能提高許多。

The fuel cell and photovoltaic (PV) play important roles in the future of power supply because they are clean and zero-radiation energy sources. Therefore, many advanced countries have invested heavily in these technologies to reduce the dependence on fossil fuels or nuclear energy. To maximize effectiveness, the two energy sources need to be combined with DC/DC converter and digital controller.
This work employs fuzzy logic control for hybrid fuel cell and PV power systems. In the hybrid fuel cell system, the fuzzy sliding surface control implemented through a digital signal processor is used to simplify the rule base and achieve stable output voltage under different load situations. Experimental results show that the output voltage of the hybrid fuel cell system is kept stable at a constant value even though the rule number is only a square root multiplied in relation to the standard fuzzy control. Because of the simpler rule base of the sliding surface fuzzy control, the gate count of logic synthesis with a field-programmable gate array (FPGA) can be reduced. Based on experimental results, fuzzy sliding surface control is successfully applied to a FPGA system to achieve stable output voltage. Subsequently, the proposed algorithm is simulated and tested on hybrid fuel cell systems in digital workstations. Simulation results based on the register transfer level (RTL) are highly consistent with our design target.
In PV power systems, output efficiency is determined by the performance of maximum power point tracking (MPPT) algorithms. The perturbation and observation method (P&O) and the incremental conductance method (INC) are commonly adopted in PV power systems. The above methods have slower speeds for tracking the optimal operating point and exhibit continuous oscillation around the optimal operating point which leads to power loss and system instability. Therefore, type-1 fuzzy control has been introduced to increase tracking speeds and reduce the steady-state oscillation of P&O and INC algorithms. However, this fuzzy control cannot suppress the noise. Here, the interval type-2 fuzzy control algorithm is chosen for the MPPT because it provides faster tracking capability and prevents noise interference. Experimental results indicate that the efficiencies achieved by the interval type-2 fuzzy control algorithm are 98.9% under constant weather conditions and 99.2% under varying weather conditions, which are more effective compared to those of INC and type-1 fuzzy control algorithms.

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