本研究主要是針對兩種多變數最大功率點追蹤法，應用在分散式太陽能發電系統上之效能評估與比較。此兩種演算法分別為最陡梯度法(Steepest Descent Method)和粒子群演算法(Particle Swarm Optimization ,PSO)，由於多變最大功率點追蹤法應用於分散式太陽能發電系統時可有效的降低系統所需之硬體元件數量，進而降低分散式發電系統建構之成本，因此驗證此類演算法可有效廣泛的應用於系統上，有助於太陽能發電系統之發展。在過去研究中，此兩種方法大多都是適用於單純串聯或並聯之分散式系統，而本文將針對同時存在串並聯組合之太陽能電池模組發電系統來建構模擬系統已進行MPPT追蹤實驗。
在模擬部分採用了Multisim電路模擬軟體與Labview程式來建構出模擬系統，利用Multisim程式可模擬出系統電路在追蹤過程中之暫態變化以評估其與實際應體搭配之可行性，而利用Labview軟體則是可較迅速的模擬出多次追蹤過程和多種串並聯組合，也評估出演算法在各種架構下之皆可達到一定之效能。
實驗部分則是採用實驗室CSSS-090A之太陽能電池，來進行四片太陽能電池模組串聯和分別將兩模組各自串聯後再將其並聯此兩種架構下，兩種演算法之追蹤成效，驗證此兩種演算法在分散式太陽能發電系統下之效能，並且與模擬結果比較驗證模擬之可靠度。

The research evaluates and compares the application of two Multivariate Maximal Power Point Tracking Methods on Distributed Photovoltaic System. These two algorithms are Steepest Descent Method and Particle Swarm Optimization (PSO). Since these types of tracking methods reduce the elements needed in distributed photovoltaic system hardware, the practical application would then reduce the cost of photovoltaic system. The paper will focus on the MPPT experiments of the stimulation systems according to different series-parallel combinations of photovoltaic system.

There are two programs used to simulate systems: Multisim and Labview; in order to simulate transient changes of circuit and then further evaluate the best algorithm and hardware collocations. In addition, through tracking with various series-parallel combinations, the algorithm can achieve certain performance with different configurations.

The experiment is composed of four CSSS-090A photovoltaic panels. Through different series-parallel combinations to implement both Maximum Power Point Tracking Methods, the results show that both Steepest Descent Method and Particle Swarm Optimization are able to track, and the results are close to stimulations, while the Steepest Descent Method has shown a better tracking efficiency.

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