太陽能光伏系統在實際運轉時常會遇到部分遮蔽現象(Partial shading condition, PSC)。此現象本身會對太陽能光伏系統的輸出功率造成巨幅的下降。因此，大部分文獻會應用元啟發演算(Metaheuristic algorithm, MHA)來幫助尋找全局的最優解。而元啟發式演算法類別之中的仿生元啟發式演算法(Nature-inspired metaheuristic algorithm, NIMHA)可以在多峰的功率-電壓曲線中追蹤到最大功率點。

金雕演算法(Golden eagle optimization, GEO)是一個全新的仿生元啟發式演算法。此演算法可以解決很多需要獲得優化的問題，其中包含太陽能光伏系統的最大功率追蹤(Maximum power point tracking, MPPT)。不過原始金雕演算法的準度和追蹤時間還有可以被改善的部分。而且由於部分遮蔽的情況是會隨著時間改變的，為了針對真實環境狀況下的最大功率追蹤情況，太陽能最大功率追蹤的動態準度改善也需要一併納入考量之中。

因此，本論文中提出了一個增強型的改良金雕演算法(Enhanced modified golden eagle optimization, EMGEO)，在原始版本的金雕演算法中加入了非線性的收斂因子和鷹撲的動物行為並且透過Matlab的模擬和太陽能最大功率追蹤的實際硬體實驗，本論文中提出的增強型改良金雕演算法在實際硬體的實驗中大幅地縮短了最大功率點的追蹤時間(縮短的平均時間量約為原始金鷹演算法追蹤時間的39.45%)，而且平均上太陽能最大功率追蹤的動態準度上升大約1.78%。且相較於粒子群演算法(Particle swarm optimization, PSO)、灰狼演算法(Grey wolf optimization, GWO)以及蝙蝠演算法(Bat algorithm, BA)，平均上增強型改良金雕演算法(EMGEO)在太陽能最大功率追蹤(MPPT)的動態準度上升大約1.59%、3.24%以及3.92%。

There is one common problem often happened when solar photovoltaic (PV)
system operating in real-world situation, that is the partial shading condition (PSC). This phenomenon will cause the output power of a solar PV system drastically decrease. To ameliorate this problem, meta-heuristic algorithms (MHAs) can be applied for MPPT. One type of MHAs, which is named nature-inspired meta-heuristic algorithms (NIMHA). This type of algorithms can track the maximum power point of multiple-peak P-V curves.

Golden eagle optimization (GEO) algorithm is a new optimization algorithm
which is based on NIMHA. This algorithm can be applied to solve many problems
which need to be optimized, including solar PV system for MPPT. However, the
accuracy and tracking time in the conventional GEO can still be further improved.

Therefore, an enhanced modified golden eagle optimization (EMGEO) is proposed in this thesis. The proposed method adds the nonlinear convergence factor
and golden eagle pouncing behavior in the GEO. It is shown that, the proposed
EMGEO algorithm can drastically reduce the maximum power point of tracking
time and also enhance the dynamic tracking accuracy. On average, the proposed
EMGEO can save 39.45% of the tracking time and an improvement of 1.78% in
the dynamic tracking accuracy can be achieved, as compared to GEO. And for the
proposed EMGEO, on average, an improvement of 1.59%, 3.24% and 3.92% in the
dynamic tracking accuracy can be achieved, as compared to PSO, GWO and BA,
respectively.

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