隨著日益增長的能源需求，太陽能發電技術逐漸受到重視。對太陽能發電系統而言，操作於最大功率點是系統最理想的工作模式，而一個準確的太陽能電池模型對於開發太陽能電池最大功率追蹤技術則是不可或缺的。為了達到快速與準確的進行太陽能電池模型參數識別之目的，本文採用兩種新型的元啟發算法-水母優化算法(Jelly Fish, JS)和魔鬼魚算法(Manta Ray Foraging Optimization, MRFO)來對太陽能參數進行識別，並與蜂群算法(Artificial Bee Colony, ABC)，杜鵑鳥算法(Cuckoo Search, CS)，粒子群算法(Particle Swarm Optimization, PSO)進行比較。本文選用Panasonic VBHN330SA15太陽能電池作為參數識別之標的，並對太陽能電池單二極體模型中之光生電流(Iph)、二極體反向飽和電流(Is)、太陽能電池串聯電阻(Rs)、太陽能電池並聯電阻(Rh)、二極體理想因數( )等五個參數進行識別。本論文針對以100顆粒子疊代100次和20顆粒子疊代500次後此五參數所形成的電流—電壓特性曲線與實驗測量出的電流-電壓特性曲線間的均方根誤差(Root-Mean-Square Error, RMSE)以及收斂至最小均方根誤差的時間進行比較。由實驗結果可知，在粒子數目多時選用蜂群算法，或是在粒子數目少時選用杜鵑鳥算法、魔鬼魚算法可達到最佳的參數辨識效果。

With the ever-increasing demand for energy, solar power generation has become more and more popular. Operating at the maximum power point is the goal of solar power generation system, and an accurate solar cell model has great significance for developing the maximum power point tracking algorithms of solar cells under different conditions. In order to improve the speed and accuracy of solar cell model parameter identification, this study introduces two new meta-heuristic algorithms - jellyfish search (JS) optimization algorithm and manta ray foraging optimization (MRFO) algorithm - to identify solar parameters. Also, the algorithms are compared with the artificial bee colony (ABC) algorithm, cuckoo search (CS) algorithm, and particle swarm optimization (PSO) algorithm.
In this study, Panasonic VBHN330SA15 solar cell is chosen as the target, and five parameters, including photo-generated current (Iph), diode inverse saturation current (Is), solar cell series resistance (Rs), solar cell shunt resistance (Rh), and diode ideal factor ( ) of the single diode model, are being identified using the aforementioned meta-heuristic algorithms. After the upper and lower bounds are set, this study tests two conditions - 100 particles for 100 iterations and 20 particles for 500 iterations. Then, the I-V curves generated by these five identified parameters are compared using two performance indexes: the root-mean-square error (RMSE) between the obtained I-V curve and the I-V curve measured by the experiment, and the time required for converging to the minimum RMSE. As the simulated results suggest, ABC with a large number of particles and CS as well as MRFO, which both have a small number of particles can guarantee a fast convergence, so as to accurately identify the five parameters of the solar cell.

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