當太陽能模組部分遮蔽的情況發生，因電壓與功率特性曲線不同
而產生多峰的情形且使輸出功率大幅降低。因此需使用最大功率追蹤
(Maximum Power Tracking, MPPT)控制器，以利因不同遮陰因素下太
陽能電池依然能保持最大功率輸出。
混合演算法為近年來，學者們所研究的方向，目的是利用兩種互
補的方式以改善單一演算法的缺點。本論文提出一個結合改良型螢火
蟲演算法(Improved Firefly Algorithm, IFA)與粒子群最佳化法(Particle
Swarm Optimization, PSO)之混合法。本文利用太陽能模擬機模擬出三
條不同照度且有部分遮陰現象之特性曲線，比較本文所提出的混合法、
多粒子擾動觀察法Multiple Perturb-and-Observe(MP&O)、擾動觀察法
結合粒子群最佳化法的演算法Perturb-and-Observe+ Particle Swarm
Optimization(P&O+P.S.O)、差分演化法結合粒子群最佳化法的演算法
Different Evolution+ Particle Swarm Optimization(D.E+P.S.O)與原有粒
子群最佳化法之追蹤時間和追蹤效率。實驗結果證明本文所提出混合
法所得之效率較高，追蹤時間較短。

When partial shading conditions occur, the P-V characteristics
curves of the photovoltaic string may exhibit multiple peaks and the
operating point may not be at the global maximum power (GMP) point,
leading to low efficiency. Therefore, maximum power point tracking
(MPPT) control strategy is needed in a photovoltaic system to output the
power at the maximum.
Combing two different MPPT strategies has gained its popularity
because strategies in complementary can improve the performance of a
single algorithm. This thesis proposes a hybrid method, combing
improved firefly algorithm (IFA) and particle swarm optimization (PSO)
to capture the great attributes from both methods, and yield a better
MPPT control strategy. In order to justify the proposed method, we
compared it with same of the state of the art such as Multiple
Perturb-and-Observe, Perturb-and-Observe+Particle Swarm Optimization,
Different Evolution+ Particle Swarm Optimization and PSO. All the
verifications were done experimentally and the results showed that the
proposed hybrid method yields the highest efficiency and the shortest
tracking time.

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