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，以利於不同因素下太陽能電池依然能保持最大功率輸出。
由於傳統集中式太陽能系統會因遮蔽效應、模組劣化而造成系統效率極低，故人們提出分散式太陽能系統。在分散式太陽能系統中，每塊太陽能板只會呈現單峰的P-V特性曲線，因此，決定性的演算法適用於分散式太陽能最大功率追蹤。本文比較並探討五種常見的分散式太陽能最大功率追蹤方法，此五種方法分別是擾動觀察法、增量電導法、多項式插值法(包含拉格朗日插值法和牛頓插值法)及斐波那契搜尋法。
為了比較各演算法的追蹤時間及不同部份遮照情形，交錯式升壓轉換器的輸入是以太陽能模擬系統依照歐規EN50530不同的部分遮照狀況進行模擬。根據實驗結果，多項式插值法在大部份實驗中，不論在最大功率點追蹤的收斂時間或效率表現都比其他演算法好。

The characteristic curve of a solar cell depends on its voltage and the current relation, which is changing with the illumination and the temperature. Owing to different 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.
Due to partial shading effects and module deterioration, the efficiency of the traditional centralized solar system is extremely low. Therefore, people have proposed to use distributed photovoltaic system (DPS). Since in DPS, each panel will only exhibit a single-peak P-V characteristic, deterministic algorithms are sufficient for MPPT tracking. In this thesis, we have compared and studied five commonly used distributed MPPT (DMPPT), and they are Perturb and Observe (P&O), Incremental conductance (INC), Polynomial Interpolation (including Newton and Lagrange), and Fibonacci search (FS).
In order to compare their tracking performance, various partial shading conditions, extracted from EN50530, were emulated by a PV simulator, which was fed to an interleaved boost converter. Based on the experimental result, in most of the cases, Newton’s method outperformed the other methods in terms of tracking time and MPPT efficiency.

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