當太陽能模組處於局部遮蔽的情況下，功率和電壓特性曲線（P-VCC）將會產生多峰的情況。如果無法獲得最大功率點，則太陽能模組的輸出功率將會大幅降低。因此目前已經開發了許多最大功率點追蹤（MPPT）控制方法來改善該問題。其中一種常見的解決方法是使用元啟發式法（MHA）來追蹤全局最大功率點（GMPP）。

最近，一種名為蝙蝠演算法（BA）的新型元啟發法在太陽能最大功率追蹤中表現良好。但當某些局部最大功率點（LMPP）太接近全局最大功率點 (GMPP) 時，蝙蝠演算法可能無法準確地追蹤到全局最大功率點。本文提出的方法將蝙蝠演算法與布穀鳥搜尋(CS)的遺棄機制結合，以提高BA的追蹤性能。於模擬和實驗結果皆表明，此方法可以在不同功率及電壓特性曲線中得到更好的效率和收斂速度。

When the solar modules are under partial shading (PS), it will make the power and voltage characteristic curves (P-VCC) have multiple peaks. If the maximum power point cannot be obtained, the output power of the solar modules will be greatly reduced. Hence, there have been various maximum power point tracking (MPPT) control methods developed to improve this problem. One of the common solutions is to use the meta-heuristic approach (MHA) to track the global maximum power point (GMPP). Recently, a new MHA called Bat algorithm (BA) has performed well in MPPT. However, BA may fail to track the GMPP when there are some local maximum power points (LMPP) are close to the GMPP. This thesis proposed to combine BA with the abandonment mechanism of cuckoo search (CS) to improve the tracking performance of the BA. The simulation and experimental results show that the proposed method yield better accuracy and convergence speed for various P-VCCs.

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