太陽能電池依其電壓與電流之關係可繪出非線性之特性曲線，該曲線會隨日照與溫度改變，依據太陽能電池當下的狀態，有不同的電壓-電流(V-I)特性曲線而有不同的功率輸出，因而需發展出最大功率(Maximum Power Point Tracking, MPPT)追蹤系統，以利於不同遮蔭因素下太陽能電池依然能保持最大功率輸出。
本論文利用太陽能模擬機模擬出太陽能電池因部份遮蔽導致輸出功率-電壓(P-V)特性曲線的多峰現象，此現象會在特性曲線上產生多個區域的最佳解(local maximum)，傳統的方法無法辨別是否為全域最佳解(global maximum)，而造成無法有效的輸出最大功率，因此本文使用三種最佳化的智能演算法，做最大功率點的搜尋，分別為粒子群演算法(particle swarm optimization, PSO)、模擬退火法(simulated annealing, SA)與多粒子擾動觀察法(multiple-perturb-and-observe, multiple-P&O)。模擬退火法為單一粒子搜尋的智能演算法，且為全域最佳解之搜尋方法，可成功的追蹤到最大功率點，其方法運算簡單，但需較長的搜尋時間。粒子群演算法為群體粒子之隨機搜尋演算法，可成功搜尋到最大功率點，但因其演算法有隨機亂數機制會影響收斂時間。為了克服隨機亂數可能會影響收斂時間的缺點，本文提出多粒子擾動觀察法，以直接搜尋且用群體粒子做最大功率點搜尋，不但時間快且不受亂數機制影響收斂時間。

The characteristic curve of a solar cell depends on its voltage and the current relation, which is dependent on the insulation and the temperature change. Owning to different shading conditions, each solar cell has different voltage-current (V-I) characteristic curve exhibiting different maximum power point. Therefore, maximum power point tracking (MPPT) system must be developed for tracking the maximum power under different shading conditions.
The power-voltage (P-V) characteristic curve has multiple peaks because of partially shading. This thesis proposes three kinds of maximum power point tracking methods which are multiple perturb-and-observe (P&O), simulated annealing (SA) and particle swarm optimization (PSO) for a photovoltaic system to track the global maximum point under partially shaded conditions.
SA is a kind of heuristic algorithm, able to track the maximum power point. The method is simple but takes a long time for the global maximum point (GMP) searching. PSO is also capable of tracking the maximum power point. Nevertheless, PSO may also take a long time to converge. In this thesis, a multiple perturb-and-observe method to allocate GMP. This method is a scenario of experiment results showing that the proposed method is fast and able to track GMP effectively.

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